bump: version 0.46.1 → 0.46.2

fix(smithery): Enable JSON response format for scanner compatibility

.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 +1,67 @@
+## v0.46.2 (2025-11-22)
+
+### Fix
+
+- **smithery**: Enable JSON response format for scanner compatibility
+
+## 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"]

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: 0.46.2
+appVersion: "0.46.2"
 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/app.py

@@ -1072,7 +1072,11 @@ def get_app(transport: str = "sse", enabled_apps: list[str] | None = None):
         # ADR-016: Use Smithery lifespan for stateless mode, BasicAuth otherwise
         if deployment_mode == DeploymentMode.SMITHERY_STATELESS:
             logger.info("Configuring MCP server for Smithery stateless mode")
-            mcp = FastMCP("Nextcloud MCP", lifespan=app_lifespan_smithery)
+            # json_response=True returns plain JSON-RPC instead of SSE format,
+            # required for Smithery scanner compatibility
+            mcp = FastMCP(
+                "Nextcloud MCP", lifespan=app_lifespan_smithery, json_response=True
+            )
         else:
             logger.info("Configuring MCP server for BasicAuth mode")
             mcp = FastMCP("Nextcloud MCP", lifespan=app_lifespan_basic)

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
                 )
             )
 

nextcloud_mcp_server/server/semantic.py

@@ -335,27 +335,6 @@ def configure_semantic_tools(mcp: FastMCP):
         Note: Requires MCP client to support sampling. If sampling is unavailable,
         the tool gracefully degrades to returning documents with an explanation.
         The client may prompt the user to approve the sampling request.
-
-        Examples:
-            >>> # Query about objectives across multiple apps
-            >>> result = await nc_semantic_search_answer(
-            ...     query="What are my Q1 2025 project goals?",
-            ...     ctx=ctx
-            ... )
-            >>> print(result.generated_answer)
-            "Based on Document 1 (note: Project Kickoff), Document 2 (calendar event:
-            Q1 Planning Meeting), and Document 3 (deck card: Implement semantic search),
-            your main goals are: 1) Improve semantic search accuracy by 20%,
-            2) Deploy new embedding model, 3) Reduce indexing latency..."
-
-            >>> # Query about appointments
-            >>> result = await nc_semantic_search_answer(
-            ...     query="When is my next dentist appointment?",
-            ...     ctx=ctx,
-            ...     limit=10
-            ... )
-            >>> len(result.sources)  # Calendar events and related notes
-            3
         """
         # 1. Retrieve relevant documents via existing semantic search
         search_response = await nc_semantic_search(

nextcloud_mcp_server/server/webdav.py

@@ -64,20 +64,6 @@ def configure_webdav_tools(mcp: FastMCP):
             - Text files are decoded to UTF-8
             - Documents (PDF, DOCX, etc.) are parsed and text is extracted
             - Other binary files are base64 encoded
-
-        Examples:
-            # Read a text file
-            result = await nc_webdav_read_file("Documents/readme.txt")
-            logger.info(result['content'])  # Decoded text content
-
-            # Read a PDF document (automatically parsed)
-            result = await nc_webdav_read_file("Documents/report.pdf")
-            logger.info(result['content'])  # Extracted text from PDF
-            logger.info(result['parsing_metadata'])  # Document parsing info
-
-            # Read a binary file
-            result = await nc_webdav_read_file("Images/photo.jpg")
-            logger.info(result['encoding'])  # 'base64'
         """
         client = await get_client(ctx)
         content, content_type = await client.webdav.read_file(path)

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "nextcloud-mcp-server"
-version = "0.44.0"
+version = "0.46.2"
 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 = "0.46.2"
 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" },