Update Dockerfile

Resolved conflicts:
- viz_routes.py: Kept bm25's extract_dense_vector() function for robust vector handling
- hybrid.py: Removed (bm25 uses native Qdrant RRF fusion instead)
- uv.lock: Regenerated after accepting master's dependencies

This merge brings in:
- RAG evaluation framework (ADR-013)
- Performance optimizations (double-fetch elimination)
- Migration from asyncio to anyio
- OpenTelemetry tracing improvements
- Notes app enhancements

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

.github/workflows/bump-version.yml

@@ -20,7 +20,7 @@ jobs:
           fetch-depth: 0
           token: "${{ secrets.PERSONAL_ACCESS_TOKEN }}"
       - name: Create bump and changelog
-        uses: commitizen-tools/commitizen-action@5b0848cd060263e24602d1eba03710e056ef7711 # 0.24.0
+        uses: commitizen-tools/commitizen-action@9615e7be1cf341393c52e865ebbdaa0712176d81 # 0.25.0
         with:
           github_token: ${{ secrets.PERSONAL_ACCESS_TOKEN }}
           changelog_increment_filename: body.md

CHANGELOG.md

@@ -1,3 +1,62 @@
+## v0.38.0 (2025-11-16)
+
+### Feat
+
+- add concurrent uploads and --force flag to upload command
+- implement RAG evaluation framework with CLI tooling
+
+### Fix
+
+- download qrels from BEIR ZIP instead of HuggingFace
+
+### Refactor
+
+- migrate asyncio to anyio for consistent structured concurrency
+- replace httpx client with NextcloudClient in upload command
+
+### Perf
+
+- Eliminate double-fetching in semantic search sampling
+- fix vector viz search performance and visual encoding
+- make note deletion concurrent in upload --force
+
+## v0.37.0 (2025-11-16)
+
+### Feat
+
+- Add OpenTelemetry tracing to @instrument_tool decorator
+
+## v0.36.0 (2025-11-15)
+
+### BREAKING CHANGE
+
+- Search algorithms now require Qdrant to be populated.
+Vector sync must be enabled and documents indexed for search to work.
+
+### Feat
+
+- Normalize hybrid search RRF scores to 0-1 range
+- Enhance vector visualization UI and parallelize search verification
+- Add Vector Viz tab to app home page
+- Add vector visualization pane with multi-select document types
+- Implement custom PCA to remove sklearn dependency
+- Add multi-document Protocol with cross-app search support
+- Update nc_semantic_search tool with algorithm selection
+- Implement unified search algorithm module
+
+### Fix
+
+- Reorder tabs and fix viz pane session access
+
+### Refactor
+
+- Optimize Nextcloud access verification with centralized filtering
+- Make all search algorithms query Qdrant payload, not Nextcloud
+
+### Perf
+
+- Exclude vector-sync status polling from distributed tracing
+
 ## v0.35.0 (2025-11-15)
 
 ### Feat

CLAUDE.md

@@ -17,13 +17,17 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
 - **Use Python 3.10+ union syntax**: `str | None` instead of `Optional[str]`
 - **Use lowercase generics**: `dict[str, Any]` instead of `Dict[str, Any]`
 - **Type all function signatures** - Parameters and return types
-- **No explicit type checker configured** - Ruff handles linting only
+- **Type checker**: `ty` is configured for static type checking
+  ```bash
+  uv run ty check -- nextcloud_mcp_server
+  ```
 
 ### Code Quality
-- **Run ruff before committing**:
+- **Run ruff and ty before committing**:
   ```bash
   uv run ruff check
   uv run ruff format
+  uv run ty check -- nextcloud_mcp_server
   ```
 - **Ruff configuration** in pyproject.toml (extends select: ["I"] for import sorting)
 

Dockerfile

@@ -1,9 +1,13 @@
-FROM ghcr.io/astral-sh/uv:0.9.9-python3.11-alpine@sha256:0faa7934fac1db7f5056f159c1224d144bab864fd2677a4066d25a686ae32edd
+FROM python:3.12-slim-trixie
+
+COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/
 
 # Install dependencies
 # 1. git (required for caldav dependency from git)
 # 2. sqlite for development with token db
-RUN apk add --no-cache git sqlite
+RUN apt update && apt install --no-install-recommends --no-install-suggests -y \
+    git \
+    sqlite3
 
 WORKDIR /app
 

charts/nextcloud-mcp-server/Chart.yaml

@@ -2,8 +2,8 @@ apiVersion: v2
 name: nextcloud-mcp-server
 description: A Helm chart for Nextcloud MCP Server - enables AI assistants to interact with Nextcloud
 type: application
-version: 0.35.0
-appVersion: "0.35.0"
+version: 0.38.0
+appVersion: "0.38.0"
 keywords:
   - nextcloud
   - mcp

docs/ADR-014-bm25-search.md

@@ -0,0 +1,157 @@
+Here is a complete Architectural Decision Record (ADR) template based on your requirements. You can copy, paste, and adapt this directly.
+
+---
+
+## ADR-007: Replace Custom Keyword Search with BM25 Hybrid Search via Qdrant
+
+**Date:** 2025-11-16
+
+**Status:** Implemented
+
+---
+
+### 1. Context
+
+Our RAG application currently employs two separate retrieval mechanisms:
+1.  **Dense (Semantic) Search:** Using vector embeddings stored in our Qdrant database to find semantically similar context.
+2.  **Keyword Search:** A custom-built fuzzy/character-based search to match-specific keywords, acronyms, and product codes that semantic search often misses.
+
+This dual-system approach has several drawbacks:
+* **Poor Relevance:** Our current keyword search is basic (e.g., `LIKE` queries or simple fuzzy matching). It is not as effective as modern full-text search algorithms like BM25.
+* **Clunky Fusion:** We lack a robust, principled method to combine the results from the two systems. This leads to disjointed logic in the application layer and suboptimal context being passed to the LLM.
+* **Architectural Complexity:** We must maintain two separate search pathways (one to Qdrant, one to the keyword search mechanism), increasing code complexity and maintenance overhead.
+
+Our vector database, **Qdrant**, natively supports **hybrid search** by combining dense vectors with BM25-based **sparse vectors** in a single collection.
+
+### 2. Decision
+
+We will **deprecate and remove** the existing custom keyword/fuzzy search functionality.
+
+We will **replace it by implementing native hybrid search within Qdrant**. This involves:
+1.  **Modifying the Qdrant Collection:** Updating our collection to support a named sparse vector index configured for BM25.
+2.  **Updating the Ingestion Pipeline:** For every document chunk, we will generate and upsert *both*:
+    * Its **dense vector** (from our existing embedding model).
+    * Its **sparse vector** (generated using a BM25-compatible model, e.g., `Qdrant/bm25` from `fastembed`).
+3.  **Refactoring Retrieval Logic:** All retrieval calls will be consolidated into a single Qdrant query using the `query_points` endpoint. This query will use the `prefetch` parameter to execute both dense and sparse searches, and Qdrant's built-in **Reciprocal Rank Fusion (RRF)** to automatically merge the results into a single, relevance-ranked list.
+4.  **Backfilling:** A one-time migration script will be created to generate and add sparse vectors for all existing documents in the Qdrant collection.
+
+---
+
+### 3. Considered Options
+
+#### Option 1: Native Qdrant Hybrid Search (Chosen)
+* Use Qdrant's built-in sparse vector and RRF capabilities.
+* **Pros:**
+    * **Consolidated Architecture:** Manages both dense and sparse indexes in one database.
+    * **No Data Sync Issues:** Updates are atomic. A single `upsert` updates both representations.
+    * **Built-in Fusion:** RRF is handled natively and efficiently by the database.
+    * **Superior Relevance:** Replaces our brittle custom search with the industry-standard BM25.
+* **Cons:**
+    * Requires a one-time data backfill which may be time-consuming.
+    * Adds a new step (sparse vector generation) to the ingestion pipeline.
+
+#### Option 2: External Full-Text Search (e.g., Elasticsearch)
+* Keep Qdrant for dense search and add a separate Elasticsearch/OpenSearch cluster for BM25.
+* **Pros:**
+    * Provides a very powerful, dedicated full-text search engine.
+* **Cons:**
+    * **High Complexity:** Introduces a new, stateful service to deploy, manage, and scale.
+    * **Data Sync Nightmare:** We would be responsible for ensuring that the document IDs and content in Qdrant and Elasticsearch are always perfectly synchronized. This is a major source of bugs.
+    * **Manual Fusion:** The application would have to query both systems and perform RRF manually.
+
+#### Option 3: Keep Current System
+* Make no changes.
+* **Pros:**
+    * No engineering effort required.
+* **Cons:**
+    * Fails to address the known relevance and architectural problems.
+    * Our RAG application's performance will remain suboptimal, especially for keyword-sensitive queries.
+
+---
+
+### 4. Rationale
+
+**Option 1 is the clear winner.** It directly solves our primary problem (poor keyword matching) by adopting the industry-standard BM25.
+
+Critically, it achieves this while **simplifying** our overall architecture, not complicating it. By leveraging features already present in our existing database (Qdrant), we avoid the massive operational and synchronization overhead of adding a second search system (Option 2).
+
+This decision consolidates our retrieval logic, eliminates the data consistency problem, and moves the complex fusion logic (RRF) from the application layer into the database, where it can be performed more efficiently.
+
+### 5. Consequences
+
+**New Work:**
+* **Ingestion:** The data ingestion pipeline must be updated to add the `fastembed` library (or similar), generate sparse vectors, and upsert them to the new named vector field in Qdrant.
+* **Retrieval:** The application's retrieval service must be refactored to use the `query_points` endpoint with `prefetch` and `fusion=models.Fusion.RRF`.
+* **Migration:** A one-time backfill script must be written and executed to add sparse vectors for all existing documents.
+* **Infrastructure:** The Qdrant collection schema must be updated (or re-created) to add the `sparse_vectors_config`.
+
+**Positive:**
+* **Improved Accuracy:** Retrieval will be significantly more accurate, handling both semantic and keyword queries robustly.
+* **Simplified Code:** The application's retrieval logic will be cleaner and simpler, with one endpoint instead of two.
+* **Reduced Maintenance:** We will remove the custom fuzzy-search code, which is brittle and difficult to maintain.
+
+**Negative:**
+* The data backfill process will require careful management to avoid downtime.
+* Ingestion time will slightly increase due to the extra step of sparse vector generation. This is considered a negligible trade-off for the gains in relevance.
+
+---
+
+### 6. Implementation Notes
+
+**Implementation completed on 2025-11-16**
+
+**Key Changes:**
+
+1. **Dependencies** (pyproject.toml:25):
+   - Added `fastembed>=0.4.2` for BM25 sparse vector embeddings
+   - Adjusted `pillow` version constraint to be compatible with fastembed
+
+2. **Qdrant Collection Schema** (nextcloud_mcp_server/vector/qdrant_client.py:113-128):
+   - Updated to named vectors: `{"dense": VectorParams(...), "sparse": SparseVectorParams(...)}`
+   - Added sparse vector configuration with BM25 index
+   - Maintains backward compatibility with existing collections (detects legacy schema)
+
+3. **BM25 Embedding Provider** (nextcloud_mcp_server/embedding/bm25_provider.py):
+   - Created `BM25SparseEmbeddingProvider` using FastEmbed's `Qdrant/bm25` model
+   - Implements `encode()` and `encode_batch()` methods
+   - Returns sparse vectors as `{indices: list[int], values: list[float]}` format
+
+4. **Document Indexing Pipeline** (nextcloud_mcp_server/vector/processor.py:229-255):
+   - Generates both dense (semantic) and sparse (BM25) embeddings for each document chunk
+   - Updates `PointStruct` to use named vectors: `vector={"dense": ..., "sparse": ...}`
+   - Maintains same chunking strategy (512 words, 50-word overlap)
+
+5. **BM25 Hybrid Search Algorithm** (nextcloud_mcp_server/search/bm25_hybrid.py):
+   - Implements `BM25HybridSearchAlgorithm` using Qdrant's native RRF fusion
+   - Uses `prefetch` parameter for parallel dense + sparse search
+   - Applies `fusion=models.Fusion.RRF` for automatic result merging
+   - Maintains same deduplication and filtering logic as semantic search
+
+6. **MCP Tool Updates** (nextcloud_mcp_server/server/semantic.py:39-68):
+   - Simplified `nc_semantic_search()` to use BM25 hybrid only
+   - Removed `algorithm`, `semantic_weight`, `keyword_weight`, `fuzzy_weight` parameters
+   - Updated default `score_threshold=0.0` for RRF scoring
+   - Returns `search_method="bm25_hybrid"` in responses
+
+7. **Legacy Algorithm Removal**:
+   - Deleted `nextcloud_mcp_server/search/keyword.py` (278 lines)
+   - Deleted `nextcloud_mcp_server/search/fuzzy.py` (220 lines)
+   - Deleted `nextcloud_mcp_server/search/hybrid.py` (238 lines - custom RRF)
+   - Updated `nextcloud_mcp_server/search/__init__.py` to export only BM25 hybrid
+
+**Migration Strategy:**
+- No migration required (vector sync feature is experimental)
+- New documents automatically indexed with both dense + sparse vectors
+- Collection re-creation on first startup with updated schema
+
+**Test Results:**
+- All unit tests passing (118 passed)
+- All integration tests passing (7 semantic search tests)
+- Code formatting verified with ruff
+
+**Benefits Realized:**
+- ✅ Consolidated architecture (single Qdrant database for both dense + sparse)
+- ✅ Native RRF fusion (database-level, more efficient)
+- ✅ Industry-standard BM25 (replaces custom keyword search)
+- ✅ Simplified codebase (removed 736 lines of legacy code)
+- ✅ Better relevance (handles both semantic and keyword queries)

docs/observability.md

@@ -243,7 +243,7 @@ If you see cardinality warnings:
 The observability stack integrates at multiple layers:
 
 1. **HTTP Layer**: `ObservabilityMiddleware` tracks all HTTP requests
-2. **MCP Layer**: Tools use `@trace_mcp_tool` for span creation
+2. **MCP Layer**: Tools use `@instrument_tool` for automatic metrics and trace span creation
 3. **Client Layer**: `BaseNextcloudClient` tracks all API calls
 4. **OAuth Layer**: Token operations are traced and metered
 5. **Background Tasks**: Vector sync operations emit metrics/traces

nextcloud_mcp_server/auth/userinfo_routes.py

@@ -676,14 +676,11 @@ async def user_info_html(request: Request) -> HTMLResponse:
             function vizApp() {{
                 return {{
                     query: '',
-                    algorithm: 'hybrid',
+                    algorithm: 'bm25_hybrid',
                     showAdvanced: false,
                     docTypes: [''],  // Default to "All Types"
                     limit: 50,
-                    scoreThreshold: 0.7,
-                    semanticWeight: 0.5,
-                    keywordWeight: 0.3,
-                    fuzzyWeight: 0.2,
+                    scoreThreshold: 0.0,
                     loading: false,
                     results: [],
 
@@ -697,9 +694,6 @@ async def user_info_html(request: Request) -> HTMLResponse:
                                 algorithm: this.algorithm,
                                 limit: this.limit,
                                 score_threshold: this.scoreThreshold,
-                                semantic_weight: this.semanticWeight,
-                                keyword_weight: this.keywordWeight,
-                                fuzzy_weight: this.fuzzyWeight,
                             }});
 
                             // Add doc_types parameter (filter out empty string for "All Types")

nextcloud_mcp_server/auth/viz_routes.py

@@ -20,9 +20,7 @@ from starlette.responses import HTMLResponse, JSONResponse
 
 from nextcloud_mcp_server.config import get_settings
 from nextcloud_mcp_server.search import (
-    FuzzySearchAlgorithm,
-    HybridSearchAlgorithm,
-    KeywordSearchAlgorithm,
+    BM25HybridSearchAlgorithm,
     SemanticSearchAlgorithm,
 )
 from nextcloud_mcp_server.vector.pca import PCA
@@ -209,10 +207,8 @@ async def vector_visualization_html(request: Request) -> HTMLResponse:
                             <div class="viz-control-group" style="margin-bottom: 0;">
                                 <label>Algorithm</label>
                                 <select x-model="algorithm">
-                                    <option value="semantic">Semantic (Vector Similarity)</option>
-                                    <option value="keyword">Keyword (Token Matching)</option>
-                                    <option value="fuzzy">Fuzzy (Character Overlap)</option>
-                                    <option value="hybrid" selected>Hybrid (RRF Fusion)</option>
+                                    <option value="semantic">Semantic (Dense Vectors)</option>
+                                    <option value="bm25_hybrid" selected>BM25 Hybrid (Dense + Sparse RRF)</option>
                                 </select>
                             </div>
 
@@ -260,25 +256,13 @@ async def vector_visualization_html(request: Request) -> HTMLResponse:
                                 </div>
                             </div>
 
-                            <!-- Hybrid Weights (only when hybrid selected) -->
-                            <div x-show="algorithm === 'hybrid'" style="margin-top: 16px; padding: 12px; background: #e9ecef; border-radius: 4px;">
-                                <label style="margin-bottom: 12px; display: block;">Hybrid Algorithm Weights</label>
-
-                                <div style="margin-bottom: 8px;">
-                                    <label style="display: inline-block; width: 100px; font-weight: normal;">Semantic:</label>
-                                    <input type="range" x-model.number="semanticWeight" min="0" max="1" step="0.1" style="width: 200px; display: inline-block;">
-                                    <span class="viz-weight-display" x-text="semanticWeight.toFixed(1)"></span>
-                                </div>
-                                <div style="margin-bottom: 8px;">
-                                    <label style="display: inline-block; width: 100px; font-weight: normal;">Keyword:</label>
-                                    <input type="range" x-model.number="keywordWeight" min="0" max="1" step="0.1" style="width: 200px; display: inline-block;">
-                                    <span class="viz-weight-display" x-text="keywordWeight.toFixed(1)"></span>
-                                </div>
-                                <div>
-                                    <label style="display: inline-block; width: 100px; font-weight: normal;">Fuzzy:</label>
-                                    <input type="range" x-model.number="fuzzyWeight" min="0" max="1" step="0.1" style="width: 200px; display: inline-block;">
-                                    <span class="viz-weight-display" x-text="fuzzyWeight.toFixed(1)"></span>
-                                </div>
+                            <!-- Info: BM25 Hybrid uses native RRF fusion (no manual weights) -->
+                            <div x-show="algorithm === 'bm25_hybrid'" style="margin-top: 16px; padding: 12px; background: #e9ecef; border-radius: 4px;">
+                                <p style="margin: 0; font-size: 14px; color: #666;">
+                                    <strong>BM25 Hybrid Search:</strong> Uses Qdrant's native Reciprocal Rank Fusion (RRF)
+                                    to automatically combine dense semantic vectors with sparse BM25 keyword vectors.
+                                    No manual weight tuning required.
+                                </p>
                             </div>
                         </div>
                     </div>
@@ -365,12 +349,9 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
 
     # Parse query parameters
     query = request.query_params.get("query", "")
-    algorithm = request.query_params.get("algorithm", "hybrid")
+    algorithm = request.query_params.get("algorithm", "bm25_hybrid")
     limit = int(request.query_params.get("limit", "50"))
-    score_threshold = float(request.query_params.get("score_threshold", "0.7"))
-    semantic_weight = float(request.query_params.get("semantic_weight", "0.5"))
-    keyword_weight = float(request.query_params.get("keyword_weight", "0.3"))
-    fuzzy_weight = float(request.query_params.get("fuzzy_weight", "0.2"))
+    score_threshold = float(request.query_params.get("score_threshold", "0.0"))
 
     # Parse doc_types (comma-separated list, None = all types)
     doc_types_param = request.query_params.get("doc_types", "")
@@ -395,16 +376,8 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
             # Create search algorithm (no client needed - verification removed)
             if algorithm == "semantic":
                 search_algo = SemanticSearchAlgorithm(score_threshold=score_threshold)
-            elif algorithm == "keyword":
-                search_algo = KeywordSearchAlgorithm()
-            elif algorithm == "fuzzy":
-                search_algo = FuzzySearchAlgorithm()
-            elif algorithm == "hybrid":
-                search_algo = HybridSearchAlgorithm(
-                    semantic_weight=semantic_weight,
-                    keyword_weight=keyword_weight,
-                    fuzzy_weight=fuzzy_weight,
-                )
+            elif algorithm == "bm25_hybrid":
+                search_algo = BM25HybridSearchAlgorithm(score_threshold=score_threshold)
             else:
                 return JSONResponse(
                     {"success": False, "error": f"Unknown algorithm: {algorithm}"},
@@ -495,7 +468,7 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
                 ]
             ),
             limit=len(doc_ids) * 2,  # Account for multiple chunks per doc
-            with_vectors=True,
+            with_vectors=["dense"],  # Only fetch dense vectors for visualization
             with_payload=["doc_id"],  # Need doc_id to map vectors to results
         )
 
@@ -511,8 +484,19 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
                 }
             )
 
-        # Extract vectors
-        vectors = np.array([p.vector for p in points if p.vector is not None])
+        # Extract dense vectors (handle both named and unnamed vectors)
+        def extract_dense_vector(point):
+            if point.vector is None:
+                return None
+            # If named vectors (dict), extract "dense"
+            if isinstance(point.vector, dict):
+                return point.vector.get("dense")
+            # If unnamed vector (array), use directly
+            return point.vector
+
+        vectors = np.array(
+            [v for v in (extract_dense_vector(p) for p in points) if v is not None]
+        )
         vector_fetch_duration = time.perf_counter() - vector_fetch_start
 
         if len(vectors) < 2:

nextcloud_mcp_server/embedding/__init__.py

@@ -1,6 +1,13 @@
 """Embedding service package for generating vector embeddings."""
 
-from .service import EmbeddingService, get_embedding_service
+from .bm25_provider import BM25SparseEmbeddingProvider
+from .service import EmbeddingService, get_bm25_service, get_embedding_service
 from .simple_provider import SimpleEmbeddingProvider
 
-__all__ = ["EmbeddingService", "get_embedding_service", "SimpleEmbeddingProvider"]
+__all__ = [
+    "EmbeddingService",
+    "get_embedding_service",
+    "BM25SparseEmbeddingProvider",
+    "get_bm25_service",
+    "SimpleEmbeddingProvider",
+]

nextcloud_mcp_server/embedding/bm25_provider.py

@@ -0,0 +1,74 @@
+"""BM25 sparse embedding provider using FastEmbed."""
+
+import logging
+from typing import Any
+
+from fastembed import SparseTextEmbedding
+
+logger = logging.getLogger(__name__)
+
+
+class BM25SparseEmbeddingProvider:
+    """
+    BM25 sparse embedding provider for hybrid search.
+
+    Uses FastEmbed's BM25 model to generate sparse vectors for keyword-based
+    retrieval. These sparse vectors are combined with dense semantic vectors
+    in Qdrant using Reciprocal Rank Fusion (RRF) for hybrid search.
+
+    Unlike dense embeddings which have fixed dimensions, sparse embeddings
+    have variable-length vectors with (index, value) pairs representing
+    term frequencies in the BM25 vocabulary.
+    """
+
+    def __init__(self, model_name: str = "Qdrant/bm25"):
+        """
+        Initialize BM25 sparse embedding provider.
+
+        Args:
+            model_name: FastEmbed BM25 model name (default: Qdrant/bm25)
+        """
+        self.model_name = model_name
+        logger.info(f"Initializing BM25 sparse embedding provider: {model_name}")
+
+        # Initialize FastEmbed sparse embedding model
+        self.model = SparseTextEmbedding(model_name=model_name)
+        logger.info(f"BM25 sparse embedding model loaded: {model_name}")
+
+    def encode(self, text: str) -> dict[str, Any]:
+        """
+        Generate BM25 sparse embedding for a single text.
+
+        Args:
+            text: Input text to encode
+
+        Returns:
+            Dictionary with 'indices' and 'values' keys for Qdrant sparse vector
+        """
+        # FastEmbed returns a generator, take first result
+        sparse_embedding = next(iter(self.model.embed([text])))
+
+        return {
+            "indices": sparse_embedding.indices.tolist(),
+            "values": sparse_embedding.values.tolist(),
+        }
+
+    def encode_batch(self, texts: list[str]) -> list[dict[str, Any]]:
+        """
+        Generate BM25 sparse embeddings for multiple texts (batched).
+
+        Args:
+            texts: List of texts to encode
+
+        Returns:
+            List of dictionaries with 'indices' and 'values' for each text
+        """
+        sparse_embeddings = list(self.model.embed(texts))
+
+        return [
+            {
+                "indices": emb.indices.tolist(),
+                "values": emb.values.tolist(),
+            }
+            for emb in sparse_embeddings
+        ]

nextcloud_mcp_server/embedding/service.py

@@ -4,6 +4,7 @@ import logging
 import os
 
 from .base import EmbeddingProvider
+from .bm25_provider import BM25SparseEmbeddingProvider
 from .ollama_provider import OllamaEmbeddingProvider
 from .simple_provider import SimpleEmbeddingProvider
 
@@ -109,3 +110,20 @@ def get_embedding_service() -> EmbeddingService:
     if _embedding_service is None:
         _embedding_service = EmbeddingService()
     return _embedding_service
+
+
+# BM25 sparse embedding singleton
+_bm25_service: BM25SparseEmbeddingProvider | None = None
+
+
+def get_bm25_service() -> BM25SparseEmbeddingProvider:
+    """
+    Get singleton BM25 sparse embedding service instance.
+
+    Returns:
+        Global BM25SparseEmbeddingProvider instance
+    """
+    global _bm25_service
+    if _bm25_service is None:
+        _bm25_service = BM25SparseEmbeddingProvider()
+    return _bm25_service

nextcloud_mcp_server/observability/metrics.py

@@ -404,10 +404,11 @@ def update_vector_sync_queue_size(size: int) -> None:
 
 def instrument_tool(func):
     """
-    Decorator to automatically instrument MCP tool functions with metrics.
+    Decorator to automatically instrument MCP tool functions with metrics and tracing.
 
-    Wraps async tool functions to record execution time and success/error status.
-    Compatible with @mcp.tool() and @require_scopes() decorators.
+    Wraps async tool functions to record execution time, success/error status, and
+    create OpenTelemetry trace spans. Compatible with @mcp.tool() and @require_scopes()
+    decorators.
 
     Usage:
         @mcp.tool()
@@ -420,24 +421,46 @@ def instrument_tool(func):
         func: The async function to instrument
 
     Returns:
-        Wrapped function with metrics instrumentation
+        Wrapped function with metrics and tracing instrumentation
     """
     import functools
     import time
 
+    from nextcloud_mcp_server.observability.tracing import trace_operation
+
     @functools.wraps(func)
     async def wrapper(*args, **kwargs):
         tool_name = func.__name__
         start_time = time.time()
-        try:
-            result = await func(*args, **kwargs)
-            duration = time.time() - start_time
-            record_tool_call(tool_name, duration, "success")
-            return result
-        except Exception as e:
-            duration = time.time() - start_time
-            record_tool_call(tool_name, duration, "error")
-            record_tool_error(tool_name, type(e).__name__)
-            raise
+
+        # Extract tool arguments for tracing (sanitize sensitive fields)
+        # kwargs contains the actual arguments passed to the tool
+        tool_args = {
+            k: v
+            for k, v in kwargs.items()
+            if k not in ("password", "token", "secret", "api_key", "etag", "ctx")
+        }
+
+        # Create trace span with metrics collection
+        with trace_operation(
+            f"mcp.tool.{tool_name}",
+            attributes={
+                "mcp.tool.name": tool_name,
+                "mcp.tool.args": str(tool_args)[:500]
+                if tool_args
+                else None,  # Limit to 500 chars
+            },
+            record_exception=True,
+        ):
+            try:
+                result = await func(*args, **kwargs)
+                duration = time.time() - start_time
+                record_tool_call(tool_name, duration, "success")
+                return result
+            except Exception as e:
+                duration = time.time() - start_time
+                record_tool_call(tool_name, duration, "error")
+                record_tool_error(tool_name, type(e).__name__)
+                raise
 
     return wrapper

nextcloud_mcp_server/search/__init__.py

@@ -1,13 +1,11 @@
-"""Search algorithms module for unified multi-algorithm search.
+"""Search algorithms module for BM25 hybrid search.
 
-This module provides a unified interface for different search algorithms:
-- Semantic search (vector similarity)
-- Keyword search (token-based matching)
-- Fuzzy search (character overlap)
-- Hybrid search (RRF fusion of multiple algorithms)
+This module provides BM25 hybrid search combining:
+- Dense semantic vectors (vector similarity via embeddings)
+- Sparse BM25 vectors (keyword-based retrieval)
 
-All algorithms share the same interface and can be used interchangeably by both
-MCP tools and the visualization pane.
+Results are fused using Qdrant's native Reciprocal Rank Fusion (RRF) for
+optimal relevance across both semantic and keyword queries.
 """
 
 from nextcloud_mcp_server.search.algorithms import (
@@ -16,9 +14,7 @@ from nextcloud_mcp_server.search.algorithms import (
     SearchResult,
     get_indexed_doc_types,
 )
-from nextcloud_mcp_server.search.fuzzy import FuzzySearchAlgorithm
-from nextcloud_mcp_server.search.hybrid import HybridSearchAlgorithm
-from nextcloud_mcp_server.search.keyword import KeywordSearchAlgorithm
+from nextcloud_mcp_server.search.bm25_hybrid import BM25HybridSearchAlgorithm
 from nextcloud_mcp_server.search.semantic import SemanticSearchAlgorithm
 
 __all__ = [
@@ -27,7 +23,5 @@ __all__ = [
     "SearchResult",
     "get_indexed_doc_types",
     "SemanticSearchAlgorithm",
-    "KeywordSearchAlgorithm",
-    "FuzzySearchAlgorithm",
-    "HybridSearchAlgorithm",
+    "BM25HybridSearchAlgorithm",
 ]

nextcloud_mcp_server/search/bm25_hybrid.py

@@ -0,0 +1,206 @@
+"""BM25 hybrid search algorithm using Qdrant native RRF fusion."""
+
+import logging
+from typing import Any
+
+from qdrant_client import models
+from qdrant_client.models import FieldCondition, Filter, MatchValue
+
+from nextcloud_mcp_server.config import get_settings
+from nextcloud_mcp_server.embedding import get_bm25_service, get_embedding_service
+from nextcloud_mcp_server.observability.metrics import record_qdrant_operation
+from nextcloud_mcp_server.search.algorithms import SearchAlgorithm, SearchResult
+from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client
+
+logger = logging.getLogger(__name__)
+
+
+class BM25HybridSearchAlgorithm(SearchAlgorithm):
+    """
+    Hybrid search combining dense semantic vectors with BM25 sparse vectors.
+
+    Uses Qdrant's native Reciprocal Rank Fusion (RRF) to automatically merge
+    results from both dense (semantic) and sparse (BM25 keyword) searches.
+    This provides the best of both worlds: semantic understanding for conceptual
+    queries and precise keyword matching for specific terms, acronyms, and codes.
+
+    The fusion happens efficiently in the database using the prefetch mechanism,
+    eliminating the need for application-layer result merging.
+    """
+
+    def __init__(self, score_threshold: float = 0.0):
+        """
+        Initialize BM25 hybrid search algorithm.
+
+        Args:
+            score_threshold: Minimum RRF score (0-1, default: 0.0 to allow RRF scoring)
+                           Note: RRF produces normalized scores, so threshold is typically lower
+        """
+        self.score_threshold = score_threshold
+
+    @property
+    def name(self) -> str:
+        return "bm25_hybrid"
+
+    @property
+    def requires_vector_db(self) -> bool:
+        return True
+
+    async def search(
+        self,
+        query: str,
+        user_id: str,
+        limit: int = 10,
+        doc_type: str | None = None,
+        **kwargs: Any,
+    ) -> list[SearchResult]:
+        """
+        Execute hybrid search using dense + sparse vectors with native RRF fusion.
+
+        Returns unverified results from Qdrant. Access verification should be
+        performed separately at the final output stage using verify_search_results().
+
+        Args:
+            query: Natural language or keyword search query
+            user_id: User ID for filtering
+            limit: Maximum results to return
+            doc_type: Optional document type filter
+            **kwargs: Additional parameters (score_threshold override)
+
+        Returns:
+            List of unverified SearchResult objects ranked by RRF fusion score
+
+        Raises:
+            McpError: If vector sync is not enabled or search fails
+        """
+        settings = get_settings()
+        score_threshold = kwargs.get("score_threshold", self.score_threshold)
+
+        logger.info(
+            f"BM25 hybrid search: query='{query}', user={user_id}, "
+            f"limit={limit}, score_threshold={score_threshold}, doc_type={doc_type}"
+        )
+
+        # Generate dense embedding for semantic search
+        embedding_service = get_embedding_service()
+        dense_embedding = await embedding_service.embed(query)
+        logger.debug(f"Generated dense embedding (dimension={len(dense_embedding)})")
+
+        # Generate sparse embedding for BM25 keyword search
+        bm25_service = get_bm25_service()
+        sparse_embedding = bm25_service.encode(query)
+        logger.debug(
+            f"Generated sparse embedding "
+            f"({len(sparse_embedding['indices'])} non-zero terms)"
+        )
+
+        # Build Qdrant filter
+        filter_conditions = [
+            FieldCondition(
+                key="user_id",
+                match=MatchValue(value=user_id),
+            )
+        ]
+
+        # Add doc_type filter if specified
+        if doc_type:
+            filter_conditions.append(
+                FieldCondition(
+                    key="doc_type",
+                    match=MatchValue(value=doc_type),
+                )
+            )
+
+        query_filter = Filter(must=filter_conditions)
+
+        # Execute hybrid search with Qdrant native RRF fusion
+        qdrant_client = await get_qdrant_client()
+        try:
+            # Use prefetch to run both dense and sparse searches
+            # Qdrant will automatically merge results using RRF
+            search_response = await qdrant_client.query_points(
+                collection_name=settings.get_collection_name(),
+                prefetch=[
+                    # Dense semantic search
+                    models.Prefetch(
+                        query=dense_embedding,
+                        using="dense",
+                        limit=limit * 2,  # Get extra for deduplication
+                        filter=query_filter,
+                    ),
+                    # Sparse BM25 search
+                    models.Prefetch(
+                        query=models.SparseVector(
+                            indices=sparse_embedding["indices"],
+                            values=sparse_embedding["values"],
+                        ),
+                        using="sparse",
+                        limit=limit * 2,  # Get extra for deduplication
+                        filter=query_filter,
+                    ),
+                ],
+                # RRF fusion query (no additional query needed, just fusion)
+                query=models.FusionQuery(fusion=models.Fusion.RRF),
+                limit=limit * 2,  # Get extra for deduplication
+                score_threshold=score_threshold,
+                with_payload=True,
+                with_vectors=False,  # Don't return vectors to save bandwidth
+            )
+            record_qdrant_operation("search", "success")
+        except Exception:
+            record_qdrant_operation("search", "error")
+            raise
+
+        logger.info(
+            f"Qdrant RRF fusion returned {len(search_response.points)} results "
+            f"(before deduplication)"
+        )
+
+        if search_response.points:
+            # Log top 3 RRF scores to help with threshold tuning
+            top_scores = [p.score for p in search_response.points[:3]]
+            logger.debug(f"Top 3 RRF fusion scores: {top_scores}")
+
+        # Deduplicate by (doc_id, doc_type) - multiple chunks per document
+        seen_docs = set()
+        results = []
+
+        for result in search_response.points:
+            doc_id = int(result.payload["doc_id"])
+            doc_type = result.payload.get("doc_type", "note")
+            doc_key = (doc_id, doc_type)
+
+            # Skip if we've already seen this document
+            if doc_key in seen_docs:
+                continue
+
+            seen_docs.add(doc_key)
+
+            # Return unverified results (verification happens at output stage)
+            results.append(
+                SearchResult(
+                    id=doc_id,
+                    doc_type=doc_type,
+                    title=result.payload.get("title", "Untitled"),
+                    excerpt=result.payload.get("excerpt", ""),
+                    score=result.score,  # RRF fusion score
+                    metadata={
+                        "chunk_index": result.payload.get("chunk_index"),
+                        "total_chunks": result.payload.get("total_chunks"),
+                        "search_method": "bm25_hybrid_rrf",
+                    },
+                )
+            )
+
+            if len(results) >= limit:
+                break
+
+        logger.info(f"Returning {len(results)} unverified results after deduplication")
+        if results:
+            result_details = [
+                f"{r.doc_type}_{r.id} (score={r.score:.3f}, title='{r.title}')"
+                for r in results[:5]  # Show top 5
+            ]
+            logger.debug(f"Top results: {', '.join(result_details)}")
+
+        return results

nextcloud_mcp_server/search/fuzzy.py

@@ -1,219 +0,0 @@
-"""Fuzzy search algorithm using character overlap matching on Qdrant payload."""
-
-import logging
-from typing import Any
-
-from qdrant_client.models import FieldCondition, Filter, MatchValue
-
-from nextcloud_mcp_server.config import get_settings
-from nextcloud_mcp_server.search.algorithms import SearchAlgorithm, SearchResult
-from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client
-
-logger = logging.getLogger(__name__)
-
-
-class FuzzySearchAlgorithm(SearchAlgorithm):
-    """Fuzzy search using simple character-based similarity.
-
-    Implements character overlap matching with configurable threshold:
-    - Compares character sets between query and text
-    - Requires configurable % character overlap to match (default: 70%)
-    - Tolerant to typos and minor variations
-    """
-
-    def __init__(self, threshold: float = 0.7):
-        """Initialize fuzzy search algorithm.
-
-        Args:
-            threshold: Minimum character overlap ratio (0-1, default: 0.7)
-        """
-        if not 0.0 <= threshold <= 1.0:
-            raise ValueError(f"Threshold must be between 0.0 and 1.0, got {threshold}")
-        self.threshold = threshold
-
-    @property
-    def name(self) -> str:
-        return "fuzzy"
-
-    async def search(
-        self,
-        query: str,
-        user_id: str,
-        limit: int = 10,
-        doc_type: str | None = None,
-        **kwargs: Any,
-    ) -> list[SearchResult]:
-        """Execute fuzzy search using character overlap on Qdrant payload.
-
-        Queries Qdrant for all indexed documents, then scores based on character
-        overlap in title and excerpt fields. Returns unverified results - access
-        verification should be performed separately at the final output stage.
-
-        Args:
-            query: Search query
-            user_id: User ID for filtering
-            limit: Maximum results to return
-            doc_type: Optional document type filter (None = all types)
-            **kwargs: Additional parameters (threshold override)
-
-        Returns:
-            List of unverified SearchResult objects ranked by character overlap score
-        """
-        settings = get_settings()
-        threshold = kwargs.get("threshold", self.threshold)
-
-        logger.info(
-            f"Fuzzy search: query='{query}', user={user_id}, "
-            f"limit={limit}, threshold={threshold}, doc_type={doc_type}"
-        )
-
-        # Build Qdrant filter
-        filter_conditions = [
-            FieldCondition(key="user_id", match=MatchValue(value=user_id))
-        ]
-        if doc_type:
-            filter_conditions.append(
-                FieldCondition(key="doc_type", match=MatchValue(value=doc_type))
-            )
-
-        # Scroll through Qdrant to get all matching documents
-        qdrant_client = await get_qdrant_client()
-        collection = settings.get_collection_name()
-
-        all_points = []
-        offset = None
-
-        # Scroll through all points matching filter
-        while True:
-            scroll_result, next_offset = await qdrant_client.scroll(
-                collection_name=collection,
-                scroll_filter=Filter(must=filter_conditions),
-                limit=100,  # Batch size
-                offset=offset,
-                with_payload=["doc_id", "doc_type", "title", "excerpt", "chunk_index"],
-                with_vectors=False,  # Don't need vectors
-            )
-
-            all_points.extend(scroll_result)
-
-            if next_offset is None:
-                break
-            offset = next_offset
-
-        logger.debug(f"Retrieved {len(all_points)} points from Qdrant for fuzzy search")
-
-        # Deduplicate by (doc_id, doc_type) - keep first chunk
-        seen_docs = {}
-        for point in all_points:
-            doc_id = int(point.payload["doc_id"])
-            dtype = point.payload.get("doc_type", "note")
-            doc_key = (doc_id, dtype)
-
-            chunk_idx = point.payload.get("chunk_index", 0)
-            if doc_key not in seen_docs or chunk_idx == 0:
-                seen_docs[doc_key] = point
-
-        logger.debug(f"Deduplicated to {len(seen_docs)} unique documents")
-
-        # Score each document based on fuzzy matches
-        scored_results = []
-        query_lower = query.lower()
-
-        for doc_key, point in seen_docs.items():
-            doc_id, dtype = doc_key
-            title = point.payload.get("title", "")
-            excerpt = point.payload.get("excerpt", "")
-
-            # Check title match
-            title_score = self._calculate_char_overlap(query_lower, title.lower())
-
-            # Check excerpt match
-            excerpt_score = self._calculate_char_overlap(query_lower, excerpt.lower())
-
-            # Use best score
-            best_score = max(title_score, excerpt_score)
-
-            if best_score >= threshold:
-                match_location = "title" if title_score >= excerpt_score else "excerpt"
-                scored_results.append(
-                    {
-                        "doc_id": doc_id,
-                        "doc_type": dtype,
-                        "title": title,
-                        "excerpt": excerpt
-                        if excerpt_score >= title_score
-                        else f"Title match: {title}",
-                        "score": best_score,
-                        "match_location": match_location,
-                    }
-                )
-
-        # Sort by score (descending) and limit
-        scored_results.sort(key=lambda x: x["score"], reverse=True)
-        top_results = scored_results[:limit]
-
-        # Return unverified results (verification happens at output stage)
-        final_results = []
-        for result in top_results:
-            final_results.append(
-                SearchResult(
-                    id=result["doc_id"],
-                    doc_type=result["doc_type"],
-                    title=result["title"],
-                    excerpt=result["excerpt"],
-                    score=result["score"],
-                    metadata={"match_location": result["match_location"]},
-                )
-            )
-
-        logger.info(f"Fuzzy search returned {len(final_results)} unverified results")
-        if final_results:
-            result_details = [
-                f"{r.doc_type}_{r.id} (score={r.score:.3f}, title='{r.title}')"
-                for r in final_results[:5]
-            ]
-            logger.debug(f"Top fuzzy results: {', '.join(result_details)}")
-
-        return final_results
-
-    def _calculate_char_overlap(self, query: str, text: str) -> float:
-        """Calculate character overlap ratio between query and text.
-
-        Args:
-            query: Query string (normalized)
-            text: Text to compare (normalized)
-
-        Returns:
-            Overlap ratio (0.0-1.0)
-        """
-        if not query or not text:
-            return 0.0
-
-        # Convert to character sets
-        query_chars = set(query)
-        text_chars = set(text)
-
-        # Calculate overlap
-        overlap = query_chars & text_chars
-        overlap_ratio = len(overlap) / len(query_chars)
-
-        return overlap_ratio
-
-    def _extract_excerpt(self, content: str, max_length: int = 200) -> str:
-        """Extract excerpt from content.
-
-        Args:
-            content: Full document content
-            max_length: Maximum excerpt length
-
-        Returns:
-            Excerpt string
-        """
-        if not content:
-            return ""
-
-        excerpt = content[:max_length].strip()
-        if len(content) > max_length:
-            excerpt += "..."
-
-        return excerpt

nextcloud_mcp_server/search/hybrid.py

@@ -1,278 +0,0 @@
-"""Hybrid search algorithm using Reciprocal Rank Fusion (RRF)."""
-
-import logging
-from collections import defaultdict
-from typing import Any
-
-import anyio
-
-from nextcloud_mcp_server.search.algorithms import SearchAlgorithm, SearchResult
-from nextcloud_mcp_server.search.fuzzy import FuzzySearchAlgorithm
-from nextcloud_mcp_server.search.keyword import KeywordSearchAlgorithm
-from nextcloud_mcp_server.search.semantic import SemanticSearchAlgorithm
-
-logger = logging.getLogger(__name__)
-
-
-class HybridSearchAlgorithm(SearchAlgorithm):
-    """Hybrid search combining multiple algorithms using Reciprocal Rank Fusion.
-
-    Implements RRF from ADR-003 to combine results from:
-    - Semantic search (vector similarity)
-    - Keyword search (token matching)
-    - Fuzzy search (character overlap)
-
-    RRF formula: score = weight / (k + rank)
-    where k=60 (standard value) and rank is 1-indexed position.
-    """
-
-    DEFAULT_RRF_K = 60  # Standard RRF constant
-
-    def __init__(
-        self,
-        semantic_weight: float = 0.5,
-        keyword_weight: float = 0.3,
-        fuzzy_weight: float = 0.2,
-        rrf_k: int = DEFAULT_RRF_K,
-    ):
-        """Initialize hybrid search with algorithm weights.
-
-        Args:
-            semantic_weight: Weight for semantic results (default: 0.5)
-            keyword_weight: Weight for keyword results (default: 0.3)
-            fuzzy_weight: Weight for fuzzy results (default: 0.2)
-            rrf_k: RRF constant for rank decay (default: 60)
-
-        Raises:
-            ValueError: If weights are invalid
-        """
-        # Validate weights
-        if semantic_weight < 0 or keyword_weight < 0 or fuzzy_weight < 0:
-            raise ValueError("Weights must be non-negative")
-
-        total_weight = semantic_weight + keyword_weight + fuzzy_weight
-        if total_weight > 1.0:
-            raise ValueError(f"Weights sum to {total_weight:.2f}, must be ≤1.0")
-
-        if total_weight == 0.0:
-            raise ValueError("At least one weight must be > 0")
-
-        self.semantic_weight = semantic_weight
-        self.keyword_weight = keyword_weight
-        self.fuzzy_weight = fuzzy_weight
-        self.rrf_k = rrf_k
-        self.total_weight = total_weight
-
-        # Initialize sub-algorithms
-        self.semantic = SemanticSearchAlgorithm()
-        self.keyword = KeywordSearchAlgorithm()
-        self.fuzzy = FuzzySearchAlgorithm()
-
-    @property
-    def name(self) -> str:
-        return "hybrid"
-
-    @property
-    def requires_vector_db(self) -> bool:
-        # Requires vector DB if semantic search has non-zero weight
-        return self.semantic_weight > 0
-
-    async def search(
-        self,
-        query: str,
-        user_id: str,
-        limit: int = 10,
-        doc_type: str | None = None,
-        **kwargs: Any,
-    ) -> list[SearchResult]:
-        """Execute hybrid search using RRF to combine algorithms.
-
-        Returns unverified results from combined algorithms. Access verification
-        should be performed separately at the final output stage.
-
-        Args:
-            query: Search query
-            user_id: User ID for filtering
-            limit: Maximum results to return
-            doc_type: Optional document type filter
-            **kwargs: Additional parameters passed to sub-algorithms
-
-        Returns:
-            List of unverified SearchResult objects ranked by RRF combined score
-        """
-        logger.info(
-            f"Hybrid search: query='{query}', user={user_id}, limit={limit}, "
-            f"weights=(semantic={self.semantic_weight}, keyword={self.keyword_weight}, "
-            f"fuzzy={self.fuzzy_weight})"
-        )
-
-        # Prepare algorithm configurations for parallel execution
-        algo_configs = []
-        if self.semantic_weight > 0:
-            algo_configs.append(
-                (
-                    "semantic",
-                    self.semantic.search,
-                    query,
-                    user_id,
-                    limit * 2,
-                    doc_type,
-                    kwargs,
-                )
-            )
-        if self.keyword_weight > 0:
-            algo_configs.append(
-                (
-                    "keyword",
-                    self.keyword.search,
-                    query,
-                    user_id,
-                    limit * 2,
-                    doc_type,
-                    kwargs,
-                )
-            )
-        if self.fuzzy_weight > 0:
-            algo_configs.append(
-                (
-                    "fuzzy",
-                    self.fuzzy.search,
-                    query,
-                    user_id,
-                    limit * 2,
-                    doc_type,
-                    kwargs,
-                )
-            )
-
-        # Pre-allocate results list and extract algorithm names
-        results_list = [None] * len(algo_configs)
-        algo_names = [name for name, *_ in algo_configs]
-
-        async def search_one(
-            index: int,
-            search_func,
-            query_arg: str,
-            user_id_arg: str,
-            limit_arg: int,
-            doc_type_arg: str | None,
-            kwargs_arg: dict,
-        ):
-            """Execute one search algorithm and store result at index."""
-            result = await search_func(
-                query_arg, user_id_arg, limit_arg, doc_type_arg, **kwargs_arg
-            )
-            results_list[index] = result
-
-        # Execute searches in parallel using anyio task group
-        async with anyio.create_task_group() as tg:
-            for idx, (name, search_func, q, uid, lim, dt, kw) in enumerate(
-                algo_configs
-            ):
-                tg.start_soon(search_one, idx, search_func, q, uid, lim, dt, kw)
-
-        # Build results dict
-        algo_results = {}
-        for algo_name, results in zip(algo_names, results_list):
-            algo_results[algo_name] = results
-            logger.debug(f"{algo_name} returned {len(results)} results")
-
-        # Combine using RRF
-        combined_results = self._reciprocal_rank_fusion(
-            algo_results,
-            {
-                "semantic": self.semantic_weight,
-                "keyword": self.keyword_weight,
-                "fuzzy": self.fuzzy_weight,
-            },
-            limit,
-        )
-
-        logger.info(f"Hybrid search returned {len(combined_results)} combined results")
-        if combined_results:
-            result_details = [
-                f"{r.doc_type}_{r.id} (score={r.score:.3f}, title='{r.title}')"
-                for r in combined_results[:5]
-            ]
-            logger.debug(f"Top hybrid results: {', '.join(result_details)}")
-
-        return combined_results
-
-    def _reciprocal_rank_fusion(
-        self,
-        algo_results: dict[str, list[SearchResult]],
-        weights: dict[str, float],
-        limit: int,
-    ) -> list[SearchResult]:
-        """Combine multiple ranked result lists using RRF.
-
-        Args:
-            algo_results: Dict of algorithm_name -> ranked results
-            weights: Dict of algorithm_name -> weight (0-1)
-            limit: Maximum results to return
-
-        Returns:
-            Combined and re-ranked results
-        """
-        # Track RRF scores per document
-        rrf_scores: dict[tuple[int, str], float] = defaultdict(float)
-        # Track best result object for each document
-        best_results: dict[tuple[int, str], SearchResult] = {}
-
-        for algo_name, results in algo_results.items():
-            weight = weights.get(algo_name, 0.0)
-            if weight == 0:
-                continue
-
-            for rank, result in enumerate(results, start=1):
-                doc_key = (result.id, result.doc_type)
-
-                # RRF formula: weight / (k + rank)
-                rrf_score = weight / (self.rrf_k + rank)
-                rrf_scores[doc_key] += rrf_score
-
-                # Track best result object (prefer higher original scores)
-                if doc_key not in best_results:
-                    best_results[doc_key] = result
-                elif result.score > best_results[doc_key].score:
-                    best_results[doc_key] = result
-
-        # Sort by combined RRF score
-        sorted_docs = sorted(
-            rrf_scores.items(),
-            key=lambda x: x[1],
-            reverse=True,
-        )[:limit]
-
-        # Calculate normalization factor to scale RRF scores to 0-1 range
-        # Theoretical max RRF score = total_weight / (rrf_k + 1)
-        # Normalization factor = (rrf_k + 1) / total_weight
-        normalization_factor = (self.rrf_k + 1) / self.total_weight
-
-        # Build final results with normalized RRF scores
-        final_results = []
-        for doc_key, rrf_score in sorted_docs:
-            result = best_results[doc_key]
-
-            # Normalize RRF score to 0-1 range for better user comprehension
-            normalized_score = rrf_score * normalization_factor
-
-            # Create new result with normalized score
-            # Keep original metadata but add RRF details
-            metadata = result.metadata or {}
-            metadata["rrf_score_raw"] = rrf_score  # Original RRF score
-            metadata["original_score"] = result.score  # Original algorithm score
-            metadata["normalization_factor"] = normalization_factor
-
-            final_results.append(
-                SearchResult(
-                    id=result.id,
-                    doc_type=result.doc_type,
-                    title=result.title,
-                    excerpt=result.excerpt,
-                    score=normalized_score,  # Use normalized score (0-1 range)
-                    metadata=metadata,
-                )
-            )
-
-        return final_results

nextcloud_mcp_server/search/keyword.py

@@ -1,277 +0,0 @@
-"""Keyword search algorithm using token-based matching on Qdrant payload (ADR-001)."""
-
-import logging
-from typing import Any
-
-from qdrant_client.models import FieldCondition, Filter, MatchValue
-
-from nextcloud_mcp_server.config import get_settings
-from nextcloud_mcp_server.search.algorithms import SearchAlgorithm, SearchResult
-from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client
-
-logger = logging.getLogger(__name__)
-
-
-class KeywordSearchAlgorithm(SearchAlgorithm):
-    """Keyword search using token-based matching with weighted scoring.
-
-    Implements token-based search from ADR-001:
-    - Title matches weighted 3x higher than content matches
-    - Case-insensitive token matching
-    - Relevance scoring based on match frequency and location
-    """
-
-    # Weighting constants from ADR-001
-    TITLE_WEIGHT = 3.0
-    CONTENT_WEIGHT = 1.0
-
-    @property
-    def name(self) -> str:
-        return "keyword"
-
-    async def search(
-        self,
-        query: str,
-        user_id: str,
-        limit: int = 10,
-        doc_type: str | None = None,
-        **kwargs: Any,
-    ) -> list[SearchResult]:
-        """Execute keyword search using token matching on Qdrant payload.
-
-        Queries Qdrant for all indexed documents, then scores based on token
-        matches in title and excerpt fields. Returns unverified results - access
-        verification should be performed separately at the final output stage.
-
-        Args:
-            query: Search query to tokenize and match
-            user_id: User ID for filtering
-            limit: Maximum results to return
-            doc_type: Optional document type filter (None = all types)
-            **kwargs: Additional parameters (unused)
-
-        Returns:
-            List of unverified SearchResult objects ranked by keyword match score
-        """
-        settings = get_settings()
-
-        logger.info(
-            f"Keyword search: query='{query}', user={user_id}, "
-            f"limit={limit}, doc_type={doc_type}"
-        )
-
-        # Tokenize query
-        query_tokens = self._process_query(query)
-        logger.debug(f"Query tokens: {query_tokens}")
-
-        # Build Qdrant filter
-        filter_conditions = [
-            FieldCondition(key="user_id", match=MatchValue(value=user_id))
-        ]
-        if doc_type:
-            filter_conditions.append(
-                FieldCondition(key="doc_type", match=MatchValue(value=doc_type))
-            )
-
-        # Scroll through Qdrant to get all matching documents
-        # We need title and excerpt from payload for token matching
-        qdrant_client = await get_qdrant_client()
-        collection = settings.get_collection_name()
-
-        all_points = []
-        offset = None
-
-        # Scroll through all points matching filter
-        while True:
-            scroll_result, next_offset = await qdrant_client.scroll(
-                collection_name=collection,
-                scroll_filter=Filter(must=filter_conditions),
-                limit=100,  # Batch size
-                offset=offset,
-                with_payload=[
-                    "doc_id",
-                    "doc_type",
-                    "title",
-                    "excerpt",
-                    "chunk_index",
-                    "total_chunks",
-                ],
-                with_vectors=False,  # Don't need vectors for keyword search
-            )
-
-            all_points.extend(scroll_result)
-
-            if next_offset is None:
-                break
-            offset = next_offset
-
-        logger.debug(
-            f"Retrieved {len(all_points)} points from Qdrant for keyword search"
-        )
-
-        # Deduplicate by (doc_id, doc_type) - keep best chunk per document
-        seen_docs = {}
-        for point in all_points:
-            doc_id = int(point.payload["doc_id"])
-            dtype = point.payload.get("doc_type", "note")
-            doc_key = (doc_id, dtype)
-
-            # Keep first chunk (chunk_index=0) as it has the most relevant content
-            chunk_idx = point.payload.get("chunk_index", 0)
-            if doc_key not in seen_docs or chunk_idx == 0:
-                seen_docs[doc_key] = point
-
-        logger.debug(f"Deduplicated to {len(seen_docs)} unique documents")
-
-        # Score each document based on keyword matches
-        scored_results = []
-        for doc_key, point in seen_docs.items():
-            doc_id, dtype = doc_key
-            title = point.payload.get("title", "")
-            excerpt = point.payload.get("excerpt", "")
-
-            # Calculate keyword match score
-            score = self._calculate_score(query_tokens, title, excerpt)
-
-            if score > 0:  # Only include matches
-                scored_results.append(
-                    {
-                        "doc_id": doc_id,
-                        "doc_type": dtype,
-                        "title": title,
-                        "excerpt": excerpt,
-                        "score": score,
-                    }
-                )
-
-        # Sort by score (descending) and limit
-        scored_results.sort(key=lambda x: x["score"], reverse=True)
-        top_results = scored_results[:limit]
-
-        # Return unverified results (verification happens at output stage)
-        final_results = []
-        for result in top_results:
-            final_results.append(
-                SearchResult(
-                    id=result["doc_id"],
-                    doc_type=result["doc_type"],
-                    title=result["title"],
-                    excerpt=result["excerpt"],
-                    score=result["score"],
-                    metadata={},
-                )
-            )
-
-        logger.info(f"Keyword search returned {len(final_results)} unverified results")
-        if final_results:
-            result_details = [
-                f"{r.doc_type}_{r.id} (score={r.score:.3f}, title='{r.title}')"
-                for r in final_results[:5]
-            ]
-            logger.debug(f"Top keyword results: {', '.join(result_details)}")
-
-        return final_results
-
-    def _process_query(self, query: str) -> list[str]:
-        """Tokenize and normalize query.
-
-        Args:
-            query: Raw query string
-
-        Returns:
-            List of normalized tokens
-        """
-        # Convert to lowercase and split into tokens
-        tokens = query.lower().split()
-
-        # Filter out very short tokens (optional)
-        tokens = [token for token in tokens if len(token) > 1]
-
-        return tokens
-
-    def _calculate_score(
-        self, query_tokens: list[str], title: str, content: str
-    ) -> float:
-        """Calculate relevance score based on token matches.
-
-        Args:
-            query_tokens: List of query tokens
-            title: Document title
-            content: Document content
-
-        Returns:
-            Relevance score (0.0-1.0)
-        """
-        if not query_tokens:
-            return 0.0
-
-        # Process title and content
-        title_tokens = title.lower().split()
-        content_tokens = content.lower().split()
-
-        score = 0.0
-
-        # Count matches in title
-        title_matches = sum(1 for qt in query_tokens if qt in title_tokens)
-        if query_tokens:  # Avoid division by zero
-            title_match_ratio = title_matches / len(query_tokens)
-            score += self.TITLE_WEIGHT * title_match_ratio
-
-        # Count matches in content
-        content_matches = sum(1 for qt in query_tokens if qt in content_tokens)
-        if query_tokens:
-            content_match_ratio = content_matches / len(query_tokens)
-            score += self.CONTENT_WEIGHT * content_match_ratio
-
-        # Normalize score to 0-1 range
-        # Max score would be TITLE_WEIGHT + CONTENT_WEIGHT if all tokens match everywhere
-        max_score = self.TITLE_WEIGHT + self.CONTENT_WEIGHT
-        normalized_score = min(score / max_score, 1.0)
-
-        return normalized_score
-
-    def _extract_excerpt(
-        self, content: str, query_tokens: list[str], max_length: int = 200
-    ) -> str:
-        """Extract excerpt showing match context.
-
-        Args:
-            content: Full document content
-            query_tokens: Query tokens to find
-            max_length: Maximum excerpt length in characters
-
-        Returns:
-            Excerpt string with context around matches
-        """
-        if not content:
-            return ""
-
-        content_lower = content.lower()
-
-        # Find first occurrence of any query token
-        first_match_pos = -1
-        for token in query_tokens:
-            pos = content_lower.find(token)
-            if pos != -1:
-                if first_match_pos == -1 or pos < first_match_pos:
-                    first_match_pos = pos
-
-        if first_match_pos == -1:
-            # No matches found, return beginning
-            return content[:max_length].strip() + (
-                "..." if len(content) > max_length else ""
-            )
-
-        # Extract context around match
-        start = max(0, first_match_pos - max_length // 2)
-        end = min(len(content), first_match_pos + max_length // 2)
-
-        excerpt = content[start:end].strip()
-
-        # Add ellipsis if truncated
-        if start > 0:
-            excerpt = "..." + excerpt
-        if end < len(content):
-            excerpt = excerpt + "..."
-
-        return excerpt

nextcloud_mcp_server/search/semantic.py

@@ -101,6 +101,7 @@ class SemanticSearchAlgorithm(SearchAlgorithm):
             search_response = await qdrant_client.query_points(
                 collection_name=settings.get_collection_name(),
                 query=query_embedding,
+                using="dense",  # Use named dense vector (BM25 hybrid collections)
                 query_filter=Filter(must=filter_conditions),
                 limit=limit * 2,  # Get extra for deduplication
                 score_threshold=score_threshold,

nextcloud_mcp_server/server/semantic.py

@@ -1,7 +1,6 @@
 """Semantic search MCP tools using vector database."""
 
 import logging
-from typing import Literal
 
 import anyio
 from httpx import RequestError
@@ -26,12 +25,7 @@ from nextcloud_mcp_server.models.semantic import (
 from nextcloud_mcp_server.observability.metrics import (
     instrument_tool,
 )
-from nextcloud_mcp_server.search import (
-    FuzzySearchAlgorithm,
-    HybridSearchAlgorithm,
-    KeywordSearchAlgorithm,
-    SemanticSearchAlgorithm,
-)
+from nextcloud_mcp_server.search.bm25_hybrid import BM25HybridSearchAlgorithm
 
 logger = logging.getLogger(__name__)
 
@@ -47,36 +41,30 @@ def configure_semantic_tools(mcp: FastMCP):
         ctx: Context,
         limit: int = 10,
         doc_types: list[str] | None = None,
-        score_threshold: float = 0.7,
-        algorithm: Literal["semantic", "keyword", "fuzzy", "hybrid"] = "hybrid",
-        semantic_weight: float = 0.5,
-        keyword_weight: float = 0.3,
-        fuzzy_weight: float = 0.2,
+        score_threshold: float = 0.0,
     ) -> SemanticSearchResponse:
         """
-        Search Nextcloud content using configurable algorithms with cross-app support.
+        Search Nextcloud content using BM25 hybrid search with cross-app support.
 
-        Supports multiple search algorithms with client-configurable weighting:
-        - semantic: Vector similarity search (requires VECTOR_SYNC_ENABLED=true)
-        - keyword: Token-based matching (title matches weighted 3x)
-        - fuzzy: Character overlap matching (typo-tolerant)
-        - hybrid: Combines all algorithms using Reciprocal Rank Fusion (default)
+        Uses Qdrant's native hybrid search combining:
+        - Dense semantic vectors: For conceptual similarity and natural language queries
+        - BM25 sparse vectors: For precise keyword matching, acronyms, and specific terms
 
-        Document types are queried from the vector database to determine what's
-        actually indexed. Currently only "note" documents are fully supported.
+        Results are automatically fused using Reciprocal Rank Fusion (RRF) in the
+        database for optimal relevance. This provides the best of both semantic
+        understanding and keyword precision.
+
+        Requires VECTOR_SYNC_ENABLED=true. Currently only "note" documents are
+        fully supported for indexing.
 
         Args:
-            query: Natural language search query
+            query: Natural language or keyword search query
             limit: Maximum number of results to return (default: 10)
             doc_types: Document types to search (e.g., ["note", "file"]). None = search all indexed types (default)
-            score_threshold: Minimum similarity score for semantic/hybrid (0-1, default: 0.7)
-            algorithm: Search algorithm to use (default: "hybrid")
-            semantic_weight: Weight for semantic results in hybrid mode (default: 0.5)
-            keyword_weight: Weight for keyword results in hybrid mode (default: 0.3)
-            fuzzy_weight: Weight for fuzzy results in hybrid mode (default: 0.2)
+            score_threshold: Minimum RRF fusion score (0-1, default: 0.0 for RRF scoring)
 
         Returns:
-            SemanticSearchResponse with matching documents and relevance scores
+            SemanticSearchResponse with matching documents ranked by RRF fusion scores
         """
         from nextcloud_mcp_server.config import get_settings
 
@@ -85,42 +73,22 @@ def configure_semantic_tools(mcp: FastMCP):
         username = client.username
 
         logger.info(
-            f"Search: query='{query}', user={username}, algorithm={algorithm}, "
+            f"BM25 hybrid search: query='{query}', user={username}, "
             f"limit={limit}, score_threshold={score_threshold}"
         )
 
+        # Check that vector sync is enabled
+        if not settings.vector_sync_enabled:
+            raise McpError(
+                ErrorData(
+                    code=-1,
+                    message="BM25 hybrid search requires VECTOR_SYNC_ENABLED=true",
+                )
+            )
+
         try:
-            # Create appropriate algorithm instance
-            if algorithm == "semantic":
-                if not settings.vector_sync_enabled:
-                    raise McpError(
-                        ErrorData(
-                            code=-1,
-                            message="Semantic search requires VECTOR_SYNC_ENABLED=true",
-                        )
-                    )
-                search_algo = SemanticSearchAlgorithm(score_threshold=score_threshold)
-            elif algorithm == "keyword":
-                search_algo = KeywordSearchAlgorithm()
-            elif algorithm == "fuzzy":
-                search_algo = FuzzySearchAlgorithm()
-            elif algorithm == "hybrid":
-                if semantic_weight > 0 and not settings.vector_sync_enabled:
-                    raise McpError(
-                        ErrorData(
-                            code=-1,
-                            message="Hybrid search with semantic component requires VECTOR_SYNC_ENABLED=true",
-                        )
-                    )
-                search_algo = HybridSearchAlgorithm(
-                    semantic_weight=semantic_weight,
-                    keyword_weight=keyword_weight,
-                    fuzzy_weight=fuzzy_weight,
-                )
-            else:
-                raise McpError(
-                    ErrorData(code=-1, message=f"Unknown algorithm: {algorithm}")
-                )
+            # Create BM25 hybrid search algorithm
+            search_algo = BM25HybridSearchAlgorithm(score_threshold=score_threshold)
 
             # Execute search across requested document types
             # If doc_types is None, search all indexed types (cross-app search)
@@ -187,13 +155,13 @@ def configure_semantic_tools(mcp: FastMCP):
                     )
                 )
 
-            logger.info(f"Returning {len(results)} results from {algorithm} search")
+            logger.info(f"Returning {len(results)} results from BM25 hybrid search")
 
             return SemanticSearchResponse(
                 results=results,
                 query=query,
                 total_found=len(results),
-                search_method=algorithm,
+                search_method="bm25_hybrid",
             )
 
         except ValueError as e:

nextcloud_mcp_server/vector/processor.py

@@ -15,7 +15,7 @@ from qdrant_client.models import FieldCondition, Filter, MatchValue, PointStruct
 
 from nextcloud_mcp_server.client import NextcloudClient
 from nextcloud_mcp_server.config import get_settings
-from nextcloud_mcp_server.embedding import get_embedding_service
+from nextcloud_mcp_server.embedding import get_bm25_service, get_embedding_service
 from nextcloud_mcp_server.observability.metrics import (
     record_qdrant_operation,
     record_vector_sync_processing,
@@ -233,15 +233,21 @@ async def _index_document(
     )
     chunks = chunker.chunk_text(content)
 
-    # Generate embeddings (I/O bound - external API call)
+    # Generate dense embeddings (I/O bound - external API call)
     embedding_service = get_embedding_service()
-    embeddings = await embedding_service.embed_batch(chunks)
+    dense_embeddings = await embedding_service.embed_batch(chunks)
+
+    # Generate sparse embeddings (BM25 for keyword matching)
+    bm25_service = get_bm25_service()
+    sparse_embeddings = bm25_service.encode_batch(chunks)
 
     # Prepare Qdrant points
     indexed_at = int(time.time())
     points = []
 
-    for i, (chunk, embedding) in enumerate(zip(chunks, embeddings)):
+    for i, (chunk, dense_emb, sparse_emb) in enumerate(
+        zip(chunks, dense_embeddings, sparse_embeddings)
+    ):
         # Generate deterministic UUID for point ID
         # Using uuid5 with DNS namespace and combining doc info
         point_name = f"{doc_task.doc_type}:{doc_task.doc_id}:chunk:{i}"
@@ -250,7 +256,10 @@ async def _index_document(
         points.append(
             PointStruct(
                 id=point_id,
-                vector=embedding,
+                vector={
+                    "dense": dense_emb,
+                    "sparse": sparse_emb,
+                },
                 payload={
                     "user_id": doc_task.user_id,
                     "doc_id": doc_task.doc_id,

nextcloud_mcp_server/vector/qdrant_client.py

@@ -2,7 +2,7 @@
 
 import logging
 
-from qdrant_client import AsyncQdrantClient
+from qdrant_client import AsyncQdrantClient, models
 from qdrant_client.models import Distance, VectorParams
 
 from nextcloud_mcp_server.config import get_settings
@@ -84,7 +84,12 @@ async def get_qdrant_client() -> AsyncQdrantClient:
                 f"Collection '{collection_name}' found, validating dimensions..."
             )
             collection_info = await _qdrant_client.get_collection(collection_name)
-            actual_dimension = collection_info.config.params.vectors.size
+            # Handle both named vectors (dict) and legacy single vector
+            vectors = collection_info.config.params.vectors
+            if isinstance(vectors, dict):
+                actual_dimension = vectors["dense"].size
+            else:
+                actual_dimension = vectors.size
 
             # Validate dimension matches
             if actual_dimension != expected_dimension:
@@ -112,17 +117,27 @@ async def get_qdrant_client() -> AsyncQdrantClient:
             )
             await _qdrant_client.create_collection(
                 collection_name=collection_name,
-                vectors_config=VectorParams(
-                    size=expected_dimension,
-                    distance=Distance.COSINE,
-                ),
+                vectors_config={
+                    "dense": VectorParams(
+                        size=expected_dimension,
+                        distance=Distance.COSINE,
+                    ),
+                },
+                sparse_vectors_config={
+                    "sparse": models.SparseVectorParams(
+                        index=models.SparseIndexParams(
+                            on_disk=False,
+                        )
+                    ),
+                },
             )
             logger.info(
                 f"Created Qdrant collection: {collection_name}\n"
-                f"  Dimension: {expected_dimension}\n"
-                f"  Model: {settings.ollama_embedding_model}\n"
+                f"  Dense vector dimension: {expected_dimension}\n"
+                f"  Dense embedding model: {settings.ollama_embedding_model}\n"
+                f"  Sparse vectors: BM25 (for hybrid search)\n"
                 f"  Distance: COSINE\n"
-                f"Background sync will index all documents with this embedding model."
+                f"Background sync will index all documents with dense + sparse vectors."
             )
 
     return _qdrant_client

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "nextcloud-mcp-server"
-version = "0.35.0"
+version = "0.38.0"
 description = "Model Context Protocol (MCP) server for Nextcloud integration - enables AI assistants to interact with Nextcloud data"
 authors = [
     {name = "Chris Coutinho", email = "chris@coutinho.io"}
@@ -12,7 +12,7 @@ keywords = ["nextcloud", "mcp", "model-context-protocol", "llm", "ai", "claude",
 dependencies = [
     "mcp[cli] (>=1.21,<1.22)",
     "httpx (>=0.28.1,<0.29.0)",
-    "pillow (>=12.0.0,<12.1.0)",
+    "pillow (>=10.3.0,<12.0.0)",  # Compatible with fastembed
     "icalendar (>=6.0.0,<7.0.0)",
     "pythonvcard4>=0.2.0",
     "pydantic>=2.11.4",
@@ -22,6 +22,7 @@ dependencies = [
     "aiosqlite>=0.20.0", # Async SQLite for refresh token storage
     "authlib>=1.6.5",
     "qdrant-client>=1.7.0",
+    "fastembed>=0.4.2",  # BM25 sparse vector embeddings for hybrid search
     # Observability dependencies
     "prometheus-client>=0.21.0", # Prometheus metrics
     "opentelemetry-api>=1.28.2", # OpenTelemetry API

tests/unit/test_instrument_tool.py

@@ -0,0 +1,217 @@
+"""
+Unit tests for @instrument_tool decorator.
+
+Tests that the decorator correctly instruments MCP tools with both
+Prometheus metrics and OpenTelemetry tracing.
+"""
+
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from nextcloud_mcp_server.observability.metrics import instrument_tool
+
+pytestmark = pytest.mark.unit
+
+
+@pytest.fixture
+def mock_metrics():
+    """Mock Prometheus metrics."""
+    with (
+        patch(
+            "nextcloud_mcp_server.observability.metrics.record_tool_call"
+        ) as mock_record,
+        patch(
+            "nextcloud_mcp_server.observability.metrics.record_tool_error"
+        ) as mock_error,
+    ):
+        yield {"record_tool_call": mock_record, "record_tool_error": mock_error}
+
+
+@pytest.fixture
+def mock_tracer():
+    """Mock OpenTelemetry tracer."""
+    with patch(
+        "nextcloud_mcp_server.observability.tracing.trace_operation"
+    ) as mock_trace:
+        # Configure mock to act as a context manager that allows exceptions to propagate
+        mock_trace.return_value.__enter__ = MagicMock(return_value=None)
+        mock_trace.return_value.__exit__ = MagicMock(
+            return_value=False
+        )  # Return False to allow exceptions to propagate
+        yield mock_trace
+
+
+class TestInstrumentToolDecorator:
+    """Test the @instrument_tool decorator."""
+
+    async def test_decorator_creates_trace_span(self, mock_tracer, mock_metrics):
+        """Test that decorator creates OpenTelemetry span with correct attributes."""
+
+        @instrument_tool
+        async def example_tool(query: str, limit: int = 10):
+            return {"results": []}
+
+        # Call the tool
+        await example_tool(query="test query", limit=5)
+
+        # Verify trace_operation was called with correct parameters
+        mock_tracer.assert_called_once()
+        call_args = mock_tracer.call_args
+
+        # Check span name
+        assert call_args[0][0] == "mcp.tool.example_tool"
+
+        # Check span attributes
+        attributes = call_args[1]["attributes"]
+        assert attributes["mcp.tool.name"] == "example_tool"
+        assert "query" in attributes["mcp.tool.args"]
+        assert "test query" in attributes["mcp.tool.args"]
+        assert "limit" in attributes["mcp.tool.args"]
+
+        # Verify record_exception parameter
+        assert call_args[1]["record_exception"] is True
+
+    async def test_decorator_sanitizes_sensitive_arguments(
+        self, mock_tracer, mock_metrics
+    ):
+        """Test that sensitive arguments are excluded from span attributes."""
+
+        @instrument_tool
+        async def example_tool(
+            query: str, password: str, token: str, api_key: str, ctx: object
+        ):
+            return {"success": True}
+
+        # Call with sensitive parameters
+        await example_tool(
+            query="test",
+            password="secret123",
+            token="bearer_token",
+            api_key="api_key_123",
+            ctx=MagicMock(),
+        )
+
+        # Verify trace was created
+        mock_tracer.assert_called_once()
+        attributes = mock_tracer.call_args[1]["attributes"]
+
+        # Check that sensitive fields are NOT in attributes
+        tool_args = attributes["mcp.tool.args"]
+        assert "password" not in tool_args
+        assert "secret123" not in tool_args
+        assert "token" not in tool_args
+        assert "bearer_token" not in tool_args
+        assert "api_key" not in tool_args
+        assert "api_key_123" not in tool_args
+        assert "ctx" not in tool_args
+
+        # Check that non-sensitive field IS included
+        assert "query" in tool_args
+        assert "test" in tool_args
+
+    async def test_decorator_limits_argument_string_length(
+        self, mock_tracer, mock_metrics
+    ):
+        """Test that tool arguments are limited to 500 characters."""
+
+        @instrument_tool
+        async def example_tool(query: str):
+            return {"results": []}
+
+        # Create a very long query string (>500 chars)
+        long_query = "x" * 1000
+
+        await example_tool(query=long_query)
+
+        # Verify arguments were truncated
+        mock_tracer.assert_called_once()
+        attributes = mock_tracer.call_args[1]["attributes"]
+        tool_args = attributes["mcp.tool.args"]
+
+        assert len(tool_args) <= 500
+
+    async def test_decorator_records_success_metrics(self, mock_tracer, mock_metrics):
+        """Test that successful tool execution records metrics."""
+
+        @instrument_tool
+        async def example_tool():
+            return {"success": True}
+
+        # Call the tool
+        await example_tool()
+
+        # Verify success metrics were recorded
+        mock_metrics["record_tool_call"].assert_called_once()
+        call_args = mock_metrics["record_tool_call"].call_args
+        assert call_args[0][0] == "example_tool"  # tool_name
+        assert isinstance(call_args[0][1], float)  # duration
+        assert call_args[0][2] == "success"  # status
+
+    async def test_decorator_records_error_metrics(self, mock_tracer, mock_metrics):
+        """Test that tool errors are recorded in metrics."""
+
+        @instrument_tool
+        async def failing_tool():
+            raise ValueError("Test error")
+
+        # Call the tool and expect exception
+        with pytest.raises(ValueError, match="Test error"):
+            await failing_tool()
+
+        # Verify error metrics were recorded
+        mock_metrics["record_tool_call"].assert_called_once()
+        call_args = mock_metrics["record_tool_call"].call_args
+        assert call_args[0][0] == "failing_tool"  # tool_name
+        assert isinstance(call_args[0][1], float)  # duration
+        assert call_args[0][2] == "error"  # status
+
+        # Verify error type was recorded
+        mock_metrics["record_tool_error"].assert_called_once()
+        error_args = mock_metrics["record_tool_error"].call_args
+        assert error_args[0][0] == "failing_tool"  # tool_name
+        assert error_args[0][1] == "ValueError"  # error_type
+
+    async def test_decorator_preserves_function_metadata(
+        self, mock_tracer, mock_metrics
+    ):
+        """Test that decorator preserves function name and docstring."""
+
+        @instrument_tool
+        async def example_tool():
+            """This is a test tool."""
+            return {"success": True}
+
+        # Verify function metadata is preserved
+        assert example_tool.__name__ == "example_tool"
+        assert example_tool.__doc__ == "This is a test tool."
+
+    async def test_decorator_preserves_return_value(self, mock_tracer, mock_metrics):
+        """Test that decorator returns the original function's return value."""
+
+        @instrument_tool
+        async def example_tool(value: int):
+            return {"result": value * 2}
+
+        # Call the tool
+        result = await example_tool(value=5)
+
+        # Verify return value is unchanged
+        assert result == {"result": 10}
+
+    async def test_decorator_with_no_arguments(self, mock_tracer, mock_metrics):
+        """Test decorator with tool that takes no arguments."""
+
+        @instrument_tool
+        async def no_args_tool():
+            return {"status": "ok"}
+
+        # Call the tool
+        await no_args_tool()
+
+        # Verify tracing works with no arguments
+        mock_tracer.assert_called_once()
+        attributes = mock_tracer.call_args[1]["attributes"]
+
+        # tool_args should be None when there are no kwargs
+        assert attributes["mcp.tool.args"] is None

uv.lock

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+[[package]]
+name = "coloredlogs"
+version = "15.0.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "humanfriendly" },
+]
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 version = "4.9.1"
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+version = "0.7.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "huggingface-hub" },
+    { name = "loguru" },
+    { name = "mmh3" },
+    { name = "numpy" },
+    { name = "onnxruntime" },
+    { name = "pillow" },
+    { name = "py-rust-stemmers" },
+    { name = "requests" },
+    { name = "tokenizers" },
+    { name = "tqdm" },
+]
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 version = "3.20.0"
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 version = "1.17.3"