Update README

perf: Optimize vector viz search performance

.github/workflows/bump-version.yml

@@ -15,7 +15,7 @@ jobs:
       packages: write
     steps:
       - name: Check out
-        uses: actions/checkout@93cb6efe18208431cddfb8368fd83d5badbf9bfd # v5
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6
         with:
           fetch-depth: 0
           token: "${{ secrets.PERSONAL_ACCESS_TOKEN }}"

.github/workflows/docker-build-publish.yml

@@ -12,7 +12,7 @@ jobs:
       packages: write
     steps:
       - name: Checkout repository
-        uses: actions/checkout@93cb6efe18208431cddfb8368fd83d5badbf9bfd # v5
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6
 
       - name: Docker meta
         id: meta

.github/workflows/helm-release.yml

@@ -14,7 +14,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout
-        uses: actions/checkout@93cb6efe18208431cddfb8368fd83d5badbf9bfd # v5
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6
         with:
           fetch-depth: 0
 

.github/workflows/release.yml

@@ -18,9 +18,9 @@ jobs:
       contents: read
     steps:
       - name: Checkout
-        uses: actions/checkout@93cb6efe18208431cddfb8368fd83d5badbf9bfd # v5
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6
       - name: Install uv
-        uses: astral-sh/setup-uv@5a7eac68fb9809dea845d802897dc5c723910fa3 # v7.1.3
+        uses: astral-sh/setup-uv@1e862dfacbd1d6d858c55d9b792c756523627244 # v7.1.4
       - name: Install Python 3.11
         run: uv python install 3.11
       - name: Build

.github/workflows/test.yml

@@ -9,9 +9,9 @@ jobs:
   linting:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@93cb6efe18208431cddfb8368fd83d5badbf9bfd # v5.0.1
+      - uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6.0.0
       - name: Install the latest version of uv
-        uses: astral-sh/setup-uv@5a7eac68fb9809dea845d802897dc5c723910fa3 # v7.1.3
+        uses: astral-sh/setup-uv@1e862dfacbd1d6d858c55d9b792c756523627244 # v7.1.4
       - name: Check format
         run: |
           uv run --frozen ruff format --diff
@@ -27,7 +27,7 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-      - uses: actions/checkout@93cb6efe18208431cddfb8368fd83d5badbf9bfd # v5.0.1
+      - uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6.0.0
         with:
           submodules: 'true'
 
@@ -56,7 +56,7 @@ jobs:
           up-flags: "--build"
 
       - name: Install the latest version of uv
-        uses: astral-sh/setup-uv@5a7eac68fb9809dea845d802897dc5c723910fa3 # v7.1.3
+        uses: astral-sh/setup-uv@1e862dfacbd1d6d858c55d9b792c756523627244 # v7.1.4
 
       - name: Install Playwright dependencies
         run: |

CHANGELOG.md

@@ -1,3 +1,61 @@
+## v0.46.1 (2025-11-22)
+
+### Perf
+
+- Optimize vector viz search performance
+
+## v0.46.0 (2025-11-22)
+
+### Feat
+
+- Add Smithery CLI deployment support
+- Implement ADR-016 Smithery stateless deployment mode
+
+### Fix
+
+- **smithery**: Add JSON Schema metadata to mcp-config endpoint
+- **smithery**: Use container runtime pattern for config discovery
+- Add Smithery lifespan and auth mode detection
+
+## v0.45.0 (2025-11-22)
+
+### Feat
+
+- Add context expansion to semantic search with chunk overlap removal
+- Use Ollama native batch API in embed_batch()
+- Implement Qdrant placeholder state management
+- Switch files to use numeric IDs with file_path resolution
+- Implement per-chunk vector visualization with context expansion
+
+### Fix
+
+- Use alpha_composite for proper RGBA highlight blending
+- Remove pymupdf.layout.activate() to fix page_chunks behavior
+- Centralize PDF processing and generate separate images per chunk
+- Set is_placeholder=False in processor to fix search filtering
+- Increase placeholder staleness threshold to 5x scan interval
+- Add placeholder staleness check to prevent duplicate processing
+- Use empty SparseVector instead of None for placeholders
+- Return empty array instead of null for query_coords when no results
+- Align PDF text extraction between indexing and context expansion
+- Update models and viz to use int-only doc_id
+- Reconstruct full content for notes to match indexed offsets
+- Add async/await, PDF metadata, and type safety fixes
+
+### Refactor
+
+- Simplify PDF text extraction with single to_markdown call
+
+### Perf
+
+- Optimize PDF processing with parallel extraction and single-render highlights
+
+## v0.44.1 (2025-11-21)
+
+### Fix
+
+- **deps**: update dependency mcp to >=1.22,<1.23
+
 ## v0.44.0 (2025-11-19)
 
 ### Feat

Dockerfile

@@ -1,6 +1,6 @@
 FROM docker.io/library/python:3.12-slim-trixie@sha256:2e683fc3e18a248aa23b8022f2a3474b072b04fb851efe9b49f6b516a8944939
 
-COPY --from=ghcr.io/astral-sh/uv:0.9.10@sha256:29bd45092ea8902c0bbb7f0a338f0494a382b1f4b18355df5be270ade679ff1d /uv /uvx /bin/
+COPY --from=ghcr.io/astral-sh/uv:0.9.11@sha256:5aa820129de0a600924f166aec9cb51613b15b68f1dcd2a02f31a500d2ede568 /uv /uvx /bin/
 
 # Install dependencies
 # 1. git (required for caldav dependency from git)
@@ -18,6 +18,7 @@ RUN uv sync --locked --no-dev --no-editable --no-cache
 
 ENV PYTHONUNBUFFERED=1
 ENV VIRTUAL_ENV=/app/.venv
+ENV PATH=/app/.vnev/bin:$PATH
 ENV TESSDATA_PREFIX=/usr/share/tesseract-ocr/5/tessdata
 
 ENTRYPOINT ["/app/.venv/bin/nextcloud-mcp-server", "--host", "0.0.0.0"]

README.md

@@ -1,11 +1,12 @@
+```markdown
 <p align="center">
   <img src="astrolabe.svg" alt="Nextcloud MCP Server" width="128" height="128">
 </p>
 
 # Nextcloud MCP Server
 
-[![Docker Image](https://img.shields.io/badge/docker-ghcr.io/cbcoutinho/nextcloud--mcp--server-blue)](https://github.com/cbcoutinho/nextcloud-mcp-server/pkgs/container/nextcloud-mcp-server)
 [![smithery badge](https://smithery.ai/badge/@cbcoutinho/nextcloud-mcp-server)](https://smithery.ai/server/@cbcoutinho/nextcloud-mcp-server)
+[![Docker Image](https://img.shields.io/badge/docker-ghcr.io/cbcoutinho/nextcloud--mcp--server-blue)](https://github.com/cbcoutinho/nextcloud-mcp-server/pkgs/container/nextcloud-mcp-server)
 
 **A production-ready MCP server that connects AI assistants to your Nextcloud instance.**
 
@@ -223,3 +224,4 @@ This project is licensed under the AGPL-3.0 License. See [LICENSE](./LICENSE) fo
 - [Model Context Protocol](https://github.com/modelcontextprotocol)
 - [MCP Python SDK](https://github.com/modelcontextprotocol/python-sdk)
 - [Nextcloud](https://nextcloud.com/)
+```

charts/nextcloud-mcp-server/Chart.lock

@@ -4,6 +4,6 @@ dependencies:
   version: 1.16.0
 - name: ollama
   repository: https://otwld.github.io/ollama-helm
-  version: 1.34.0
-digest: sha256:9dfb8d6e3d5488f669d4c37f3a766213b598ff3de2aead2c734789736c7835b4
-generated: "2025-11-17T17:08:48.055530019Z"
+  version: 1.35.0
+digest: sha256:da8db198b12ce0252df220fabb297cfe69186edb8e67952c52e05de778189b92
+generated: "2025-11-21T11:09:07.997781541Z"

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: 0.44.0
-appVersion: "0.44.0"
+version: 0.46.1
+appVersion: "0.46.1"
 keywords:
   - nextcloud
   - mcp
@@ -31,6 +31,6 @@ dependencies:
     repository: https://qdrant.github.io/qdrant-helm
     condition: qdrant.networkMode.deploySubchart
   - name: ollama
-    version: "1.34.0"
+    version: "1.35.0"
     repository: https://otwld.github.io/ollama-helm
     condition: ollama.enabled

docker-compose.yml

@@ -17,11 +17,11 @@ services:
   # Note: Redis is an external service. You can find more information about the configuration here:
   # https://hub.docker.com/_/redis
   redis:
-    image: docker.io/library/redis:alpine@sha256:5013e94192ef18a5d8368179c7522e5300f9265cc339cadac76c7b93303a2752
+    image: docker.io/library/redis:alpine@sha256:6cbef353e480a8a6e7f10ec545f13d7d3fa85a212cdcc5ffaf5a1c818b9d3798
     restart: always
 
   app:
-    image: docker.io/library/nextcloud:32.0.1@sha256:d572839eeb693026d72a0c6aa48076df0bb8930797ea321e604936ef7189d06e
+    image: docker.io/library/nextcloud:32.0.2@sha256:ac08482d73ffd85d94069ba291bbd5fb39a70ff21502030a2e3e2d89a7246a48
     restart: always
     ports:
       - 0.0.0.0:8080:80

nextcloud_mcp_server/auth/viz_routes.py

@@ -218,71 +218,41 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
                 }
             )
 
-        # Fetch vectors for specific matching chunks from Qdrant
+        # Fetch vectors for specific matching chunks from Qdrant using batch retrieve
         vector_fetch_start = time.perf_counter()
         qdrant_client = await get_qdrant_client()
 
-        # Build filters for each specific chunk
-        from qdrant_client.models import FieldCondition, Filter, MatchValue
-
         chunk_vectors_map = {}  # Map (doc_id, chunk_start, chunk_end) -> vector
 
-        # Fetch vectors in batches by filtering on chunk-specific fields
-        for result in search_results:
-            chunk_start = result.chunk_start_offset
-            chunk_end = result.chunk_end_offset
+        # Collect point IDs from search results for batch retrieval
+        # point_id is the Qdrant internal ID returned by search algorithms
+        point_ids = [r.point_id for r in search_results if r.point_id]
 
-            # Build filter for this specific chunk
-            must_conditions = [
-                get_placeholder_filter(),  # Always exclude placeholders from user-facing queries
-                FieldCondition(
-                    key="doc_id",
-                    match=MatchValue(value=result.id),
-                ),
-                FieldCondition(
-                    key="user_id",
-                    match=MatchValue(value=username),
-                ),
-            ]
-
-            # Add chunk position filters if available
-            if chunk_start is not None:
-                must_conditions.append(
-                    FieldCondition(
-                        key="chunk_start_offset",
-                        match=MatchValue(value=chunk_start),
-                    )
-                )
-            if chunk_end is not None:
-                must_conditions.append(
-                    FieldCondition(
-                        key="chunk_end_offset",
-                        match=MatchValue(value=chunk_end),
-                    )
-                )
-
-            # Fetch this specific chunk vector
-            points_response = await qdrant_client.scroll(
+        if point_ids:
+            # Single batch retrieve call instead of N sequential scroll calls
+            # This is ~50x faster for 50 results (1 HTTP request vs 50)
+            points_response = await qdrant_client.retrieve(
                 collection_name=settings.get_collection_name(),
-                scroll_filter=Filter(must=must_conditions),
-                limit=1,  # Only need the first match
+                ids=point_ids,
                 with_vectors=["dense"],
-                with_payload=False,
+                with_payload=["doc_id", "chunk_start_offset", "chunk_end_offset"],
             )
 
-            points = points_response[0]
-            if points:
-                # Extract dense vector
-                point = points[0]
+            # Build chunk_vectors_map from batch response
+            for point in points_response:
                 if point.vector is not None:
-                    # If named vectors (dict), extract "dense"
+                    # Extract dense vector (handle both named and unnamed vectors)
                     if isinstance(point.vector, dict):
                         vector = point.vector.get("dense")
                     else:
                         vector = point.vector
 
-                    chunk_key = (result.id, chunk_start, chunk_end)
-                    chunk_vectors_map[chunk_key] = vector
+                    if vector is not None and point.payload:
+                        doc_id = point.payload.get("doc_id")
+                        chunk_start = point.payload.get("chunk_start_offset")
+                        chunk_end = point.payload.get("chunk_end_offset")
+                        chunk_key = (doc_id, chunk_start, chunk_end)
+                        chunk_vectors_map[chunk_key] = vector
 
         vector_fetch_duration = time.perf_counter() - vector_fetch_start
 
@@ -341,16 +311,23 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
 
         chunk_vectors = np.array(chunk_vectors)
 
-        # Generate query embedding for visualization
+        # Reuse query embedding from search algorithm (avoids redundant embedding call)
         query_embed_start = time.perf_counter()
-        from nextcloud_mcp_server.embedding.service import get_embedding_service
+        if search_algo.query_embedding is not None:
+            query_embedding = search_algo.query_embedding
+            logger.info(
+                f"Reusing query embedding from search algorithm "
+                f"(dimension={len(query_embedding)})"
+            )
+        else:
+            # Fallback: generate embedding if not available from search
+            from nextcloud_mcp_server.embedding.service import get_embedding_service
 
-        embedding_service = get_embedding_service()
-        query_embedding = await embedding_service.embed(query)
+            embedding_service = get_embedding_service()
+            query_embedding = await embedding_service.embed(query)
+            logger.info(f"Generated query embedding (dimension={len(query_embedding)})")
         query_embed_duration = time.perf_counter() - query_embed_start
 
-        logger.info(f"Generated query embedding (dimension={len(query_embedding)})")
-
         # Combine query vector with chunk vectors for PCA
         # Query will be the last point in the array
         all_vectors = np.vstack([chunk_vectors, np.array([query_embedding])])
@@ -380,9 +357,19 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
         )
 
         # Apply PCA dimensionality reduction (768-dim → 3D) on normalized vectors
+        # Run in thread pool to avoid blocking the event loop (CPU-bound)
         pca_start = time.perf_counter()
-        pca = PCA(n_components=3)
-        coords_3d = pca.fit_transform(all_vectors_normalized)
+
+        def _compute_pca(vectors: np.ndarray) -> tuple[np.ndarray, PCA]:
+            pca = PCA(n_components=3)
+            coords = pca.fit_transform(vectors)
+            return coords, pca
+
+        import anyio
+
+        coords_3d, pca = await anyio.to_thread.run_sync(  # type: ignore[attr-defined]
+            lambda: _compute_pca(all_vectors_normalized)
+        )
         pca_duration = time.perf_counter() - pca_start
 
         # After fit, these attributes are guaranteed to be set

nextcloud_mcp_server/document_processors/pymupdf.py

@@ -6,15 +6,15 @@ import tempfile
 from collections.abc import Awaitable, Callable
 from typing import Any, Optional
 
+# NOTE: Do NOT call pymupdf.layout.activate() here!
+# It changes the behavior of pymupdf4llm.to_markdown() when page_chunks=True,
+# causing it to return a string instead of a list[dict].
+# See: https://github.com/pymupdf/pymupdf4llm/issues/323
 import pymupdf
-import pymupdf.layout
+import pymupdf4llm
 
 from .base import DocumentProcessor, ProcessingResult, ProcessorError
 
-# Activate layout analysis for better text extraction
-pymupdf.layout.activate()
-import pymupdf4llm  # noqa
-
 logger = logging.getLogger(__name__)
 
 

nextcloud_mcp_server/embedding/bm25_provider.py

@@ -37,7 +37,9 @@ class BM25SparseEmbeddingProvider:
 
     def encode(self, text: str) -> dict[str, Any]:
         """
-        Generate BM25 sparse embedding for a single text.
+        Generate BM25 sparse embedding for a single text (synchronous).
+
+        Note: For async contexts, prefer encode_async() to avoid blocking the event loop.
 
         Args:
             text: Input text to encode
@@ -53,6 +55,23 @@ class BM25SparseEmbeddingProvider:
             "values": sparse_embedding.values.tolist(),
         }
 
+    async def encode_async(self, text: str) -> dict[str, Any]:
+        """
+        Generate BM25 sparse embedding for a single text (async).
+
+        Runs CPU-bound BM25 encoding in thread pool to avoid blocking the event loop.
+
+        Args:
+            text: Input text to encode
+
+        Returns:
+            Dictionary with 'indices' and 'values' keys for Qdrant sparse vector
+        """
+        import anyio
+
+        # Run CPU-bound BM25 encoding in thread pool
+        return await anyio.to_thread.run_sync(lambda: self.encode(text))  # type: ignore[attr-defined]
+
     async def encode_batch(self, texts: list[str]) -> list[dict[str, Any]]:
         """
         Generate BM25 sparse embeddings for multiple texts (batched).

nextcloud_mcp_server/search/algorithms.py

@@ -140,6 +140,7 @@ class SearchResult:
         page_number: Page number for PDF documents (None for other doc types)
         chunk_index: Zero-based index of this chunk in the document
         total_chunks: Total number of chunks in the document
+        point_id: Qdrant point ID for batch vector retrieval (None if not from Qdrant)
     """
 
     id: int
@@ -153,6 +154,7 @@ class SearchResult:
     page_number: int | None = None
     chunk_index: int = 0
     total_chunks: int = 1
+    point_id: str | None = None
 
     def __post_init__(self):
         """Validate score is non-negative.
@@ -172,8 +174,15 @@ class SearchAlgorithm(ABC):
 
     All search algorithms must implement the search() method with consistent
     interface, allowing them to be used interchangeably.
+
+    Attributes:
+        query_embedding: The query embedding generated during the last search.
+            Available after search() completes for algorithms that use embeddings.
+            Can be reused by callers to avoid redundant embedding generation.
     """
 
+    query_embedding: list[float] | None = None
+
     @abstractmethod
     async def search(
         self,

nextcloud_mcp_server/search/bm25_hybrid.py

@@ -101,11 +101,13 @@ class BM25HybridSearchAlgorithm(SearchAlgorithm):
         # Generate dense embedding for semantic search
         embedding_service = get_embedding_service()
         dense_embedding = await embedding_service.embed(query)
+        # Store for reuse by callers (e.g., viz_routes PCA visualization)
+        self.query_embedding = dense_embedding
         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)
+        sparse_embedding = await bm25_service.encode_async(query)
         logger.debug(
             f"Generated sparse embedding "
             f"({len(sparse_embedding['indices'])} non-zero terms)"
@@ -218,6 +220,7 @@ class BM25HybridSearchAlgorithm(SearchAlgorithm):
                     page_number=result.payload.get("page_number"),
                     chunk_index=result.payload.get("chunk_index", 0),
                     total_chunks=result.payload.get("total_chunks", 1),
+                    point_id=str(result.id),  # Qdrant point ID for batch retrieval
                 )
             )
 

nextcloud_mcp_server/search/pdf_highlighter.py

@@ -845,9 +845,8 @@ class PDFHighlighter:
                             logger.warning(f"Chunk {chunk_index}: could not find bbox")
                             continue
 
-                        # Copy base image and draw highlight using PIL (fast!)
+                        # Copy base image for this chunk
                         chunk_image = base_image.copy()
-                        draw = ImageDraw.Draw(chunk_image, "RGBA")
 
                         # Scale bbox coordinates to pixmap coordinates
                         scale_x = base_pix.width / page_rect.width
@@ -859,10 +858,17 @@ class PDFHighlighter:
                             int(bbox[3] * scale_y),
                         )
 
-                        # Draw semi-transparent fill
-                        draw.rectangle(pil_bbox, fill=fill_color)
-                        # Draw dashed border (PIL doesn't support dashes, use solid)
-                        draw.rectangle(pil_bbox, outline=pil_color, width=3)
+                        # Create transparent overlay for fill (proper alpha blending)
+                        overlay = Image.new("RGBA", chunk_image.size, (0, 0, 0, 0))
+                        overlay_draw = ImageDraw.Draw(overlay)
+                        overlay_draw.rectangle(pil_bbox, fill=fill_color)
+
+                        # Alpha composite the overlay onto the chunk image
+                        chunk_image = Image.alpha_composite(chunk_image, overlay)
+
+                        # Draw border on top (solid, not transparent)
+                        border_draw = ImageDraw.Draw(chunk_image)
+                        border_draw.rectangle(pil_bbox, outline=pil_color, width=3)
 
                         # Convert back to PNG bytes
                         output = BytesIO()

nextcloud_mcp_server/search/semantic.py

@@ -78,6 +78,8 @@ class SemanticSearchAlgorithm(SearchAlgorithm):
         # Generate embedding for query
         embedding_service = get_embedding_service()
         query_embedding = await embedding_service.embed(query)
+        # Store for reuse by callers (e.g., viz_routes PCA visualization)
+        self.query_embedding = query_embedding
         logger.debug(
             f"Generated embedding for query (dimension={len(query_embedding)})"
         )
@@ -164,6 +166,7 @@ class SemanticSearchAlgorithm(SearchAlgorithm):
                     page_number=result.payload.get("page_number"),
                     chunk_index=result.payload.get("chunk_index", 0),
                     total_chunks=result.payload.get("total_chunks", 1),
+                    point_id=str(result.id),  # Qdrant point ID for batch retrieval
                 )
             )
 

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "nextcloud-mcp-server"
-version = "0.44.0"
+version = "0.46.1"
 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"}
@@ -10,7 +10,7 @@ license = {text = "AGPL-3.0-only"}
 requires-python = ">=3.11"
 keywords = ["nextcloud", "mcp", "model-context-protocol", "llm", "ai", "claude", "webdav", "caldav", "carddav"]
 dependencies = [
-    "mcp[cli] (>=1.21,<1.22)",
+    "mcp[cli] (>=1.22,<1.23)",
     "httpx (>=0.28.1,<0.29.0)",
     "pillow (>=10.3.0,<12.0.0)", # Compatible with fastembed
     "icalendar (>=6.0.0,<7.0.0)",

uv.lock

@@ -1645,7 +1645,7 @@ wheels = [
 
 [[package]]
 name = "mcp"
-version = "1.21.1"
+version = "1.22.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "anyio" },
@@ -1663,9 +1663,9 @@ dependencies = [
     { name = "typing-inspection" },
     { name = "uvicorn", marker = "sys_platform != 'emscripten'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/f7/25/4df633e7574254ada574822db2245bbee424725d1b01bccae10bf128794e/mcp-1.21.1.tar.gz", hash = "sha256:540e6ac4b12b085c43f14879fde04cbdb10148a09ea9492ff82d8c7ba651a302", size = 469071, upload-time = "2025-11-13T20:33:46.139Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/a3/a2/c5ec0ab38b35ade2ae49a90fada718fbc76811dc5aa1760414c6aaa6b08a/mcp-1.22.0.tar.gz", hash = "sha256:769b9ac90ed42134375b19e777a2858ca300f95f2e800982b3e2be62dfc0ba01", size = 471788, upload-time = "2025-11-20T20:11:28.095Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/49/af/01fb42df59ad15925ffc1e2e609adafddd3ac4572f606faae0dc8b55ba0c/mcp-1.21.1-py3-none-any.whl", hash = "sha256:dd35abe36d68530a8a1291daa25d50276d8731e545c0434d6e250a3700dd2a6d", size = 174852, upload-time = "2025-11-13T20:33:44.502Z" },
+    { url = "https://files.pythonhosted.org/packages/a9/bb/711099f9c6bb52770f56e56401cdfb10da5b67029f701e0df29362df4c8e/mcp-1.22.0-py3-none-any.whl", hash = "sha256:bed758e24df1ed6846989c909ba4e3df339a27b4f30f1b8b627862a4bade4e98", size = 175489, upload-time = "2025-11-20T20:11:26.542Z" },
 ]
 
 [package.optional-dependencies]
@@ -1936,7 +1936,7 @@ wheels = [
 
 [[package]]
 name = "nextcloud-mcp-server"
-version = "0.44.0"
+version = "0.46.1"
 source = { editable = "." }
 dependencies = [
     { name = "aiosqlite" },
@@ -1998,7 +1998,7 @@ requires-dist = [
     { name = "icalendar", specifier = ">=6.0.0,<7.0.0" },
     { name = "jinja2", specifier = ">=3.1.6" },
     { name = "langchain-text-splitters", specifier = ">=1.0.0" },
-    { name = "mcp", extras = ["cli"], specifier = ">=1.21,<1.22" },
+    { name = "mcp", extras = ["cli"], specifier = ">=1.22,<1.23" },
     { name = "opentelemetry-api", specifier = ">=1.28.2" },
     { name = "opentelemetry-exporter-otlp-proto-grpc", specifier = ">=1.28.2" },
     { name = "opentelemetry-instrumentation-asgi", specifier = ">=0.49b2" },