Update README

feature/openai provider support

.github/workflows/rag-evaluation.yml

@@ -49,7 +49,7 @@ jobs:
         env:
           # Override MCP container environment for OpenAI + vector sync
           VECTOR_SYNC_ENABLED: "true"
-          VECTOR_SYNC_SCAN_INTERVAL: "30"
+          VECTOR_SYNC_SCAN_INTERVAL: "5"
           OPENAI_API_KEY: ${{ secrets.GITHUB_TOKEN }}
           OPENAI_BASE_URL: "https://models.github.ai/inference"
           OPENAI_EMBEDDING_MODEL: ${{ inputs.embedding_model }}
@@ -79,7 +79,7 @@ jobs:
           echo "Waiting for MCP server..."
           max_attempts=30
           attempt=0
-          until curl -o /dev/null -s -w "%{http_code}\n" http://localhost:8000/health | grep -q "200"; do
+          until curl -o /dev/null -s -w "%{http_code}\n" http://localhost:8000/health/live | grep -q "200"; do
             attempt=$((attempt + 1))
             if [ $attempt -ge $max_attempts ]; then
               echo "MCP server did not become ready in time."

CHANGELOG.md

@@ -1,3 +1,20 @@
+## v0.48.2 (2025-11-23)
+
+### Fix
+
+- Share vector sync state with FastMCP session lifespan via module singleton
+- Share vector sync state with FastMCP session lifespan via module singleton
+
+## v0.48.1 (2025-11-23)
+
+### Fix
+
+- Use WebDAV for tag creation and add LLM-as-a-judge for RAG tests
+
+### Refactor
+
+- Move background tasks to server lifespan and deprecate SSE transport
+
 ## v0.48.0 (2025-11-23)
 
 ### Feat

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.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: 0.48.0
-appVersion: "0.48.0"
+version: 0.48.2
+appVersion: "0.48.2"
 keywords:
   - nextcloud
   - mcp

nextcloud_mcp_server/app.py

@@ -243,6 +243,25 @@ def validate_pkce_support(discovery: dict, discovery_url: str) -> None:
     click.echo(f"✓ PKCE support validated: {code_challenge_methods}")
 
 
+@dataclass
+class VectorSyncState:
+    """
+    Module-level state for vector sync background tasks.
+
+    This singleton bridges the Starlette server lifespan (where background tasks run)
+    and FastMCP session lifespans (where MCP tools need access to the streams).
+    """
+
+    document_send_stream: Optional[MemoryObjectSendStream] = None
+    document_receive_stream: Optional[MemoryObjectReceiveStream] = None
+    shutdown_event: Optional[anyio.Event] = None
+    scanner_wake_event: Optional[anyio.Event] = None
+
+
+# Module-level singleton for vector sync state
+_vector_sync_state = VectorSyncState()
+
+
 @dataclass
 class AppContext:
     """Application context for BasicAuth mode."""
@@ -555,15 +574,15 @@ async def load_oauth_client_credentials(
 @asynccontextmanager
 async def app_lifespan_basic(server: FastMCP) -> AsyncIterator[AppContext]:
     """
-    Manage application lifecycle for BasicAuth mode.
+    Manage application lifecycle for BasicAuth mode (FastMCP session lifespan).
 
     Creates a single Nextcloud client with basic authentication
-    that is shared across all requests.
+    that is shared across all requests within a session.
 
-    If vector sync is enabled (VECTOR_SYNC_ENABLED=true), also starts
-    background tasks for automatic document indexing (ADR-007).
+    Note: Background tasks (scanner, processor) are started at server level
+    in starlette_lifespan, not here. This lifespan runs per-session.
     """
-    logger.info("Starting MCP server in BasicAuth mode")
+    logger.info("Starting MCP session in BasicAuth mode")
     logger.info("Creating Nextcloud client with BasicAuth")
 
     client = NextcloudClient.from_env()
@@ -579,91 +598,20 @@ async def app_lifespan_basic(server: FastMCP) -> AsyncIterator[AppContext]:
     # Initialize document processors
     initialize_document_processors()
 
-    settings = get_settings()
-
-    # Check if vector sync is enabled
-    if settings.vector_sync_enabled:
-        logger.info("Vector sync enabled - starting background tasks")
-
-        # Get username from environment for BasicAuth mode
-        username = os.getenv("NEXTCLOUD_USERNAME")
-        if not username:
-            raise ValueError(
-                "NEXTCLOUD_USERNAME is required for vector sync in BasicAuth mode"
-            )
-
-        # Initialize Qdrant collection before starting background tasks
-        logger.info("Initializing Qdrant collection...")
-        from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client
-
-        try:
-            await get_qdrant_client()  # Triggers collection creation if needed
-            logger.info("Qdrant collection ready")
-        except Exception as e:
-            logger.error(f"Failed to initialize Qdrant collection: {e}")
-            raise RuntimeError(
-                f"Cannot start vector sync - Qdrant initialization failed: {e}"
-            ) from e
-
-        # Initialize shared state
-        send_stream, receive_stream = anyio.create_memory_object_stream(
-            max_buffer_size=settings.vector_sync_queue_max_size
+    # Yield client context - scanner runs at server level (starlette_lifespan)
+    # Include vector sync state from module singleton (set by starlette_lifespan)
+    try:
+        yield AppContext(
+            client=client,
+            storage=storage,
+            document_send_stream=_vector_sync_state.document_send_stream,
+            document_receive_stream=_vector_sync_state.document_receive_stream,
+            shutdown_event=_vector_sync_state.shutdown_event,
+            scanner_wake_event=_vector_sync_state.scanner_wake_event,
         )
-        shutdown_event = anyio.Event()
-        scanner_wake_event = anyio.Event()
-
-        # Start background tasks using anyio TaskGroup
-        async with anyio.create_task_group() as tg:
-            # Start scanner task
-            await tg.start(
-                scanner_task,
-                send_stream,
-                shutdown_event,
-                scanner_wake_event,
-                client,
-                username,
-            )
-
-            # Start processor pool (each gets a cloned receive stream)
-            for i in range(settings.vector_sync_processor_workers):
-                await tg.start(
-                    processor_task,
-                    i,
-                    receive_stream.clone(),
-                    shutdown_event,
-                    client,
-                    username,
-                )
-
-            logger.info(
-                f"Background sync tasks started: 1 scanner + {settings.vector_sync_processor_workers} processors"
-            )
-
-            # Yield with background tasks running
-            try:
-                yield AppContext(
-                    client=client,
-                    storage=storage,
-                    document_send_stream=send_stream,
-                    document_receive_stream=receive_stream,
-                    shutdown_event=shutdown_event,
-                    scanner_wake_event=scanner_wake_event,
-                )
-            finally:
-                # Shutdown signal
-                logger.info("Shutting down background sync tasks")
-                shutdown_event.set()
-
-                # TaskGroup automatically cancels all tasks on exit
-                logger.info("Background sync tasks stopped")
-                await client.close()
-    else:
-        # No vector sync - simple lifecycle
-        try:
-            yield AppContext(client=client, storage=storage)
-        finally:
-            logger.info("Shutting down BasicAuth mode")
-            await client.close()
+    finally:
+        logger.info("Shutting down BasicAuth session")
+        await client.close()
 
 
 async def setup_oauth_config():
@@ -979,7 +927,7 @@ async def setup_oauth_config():
     )
 
 
-def get_app(transport: str = "sse", enabled_apps: list[str] | None = None):
+def get_app(transport: str = "streamable-http", enabled_apps: list[str] | None = None):
     # Initialize observability (logging will be configured by uvicorn)
     settings = get_settings()
 
@@ -1197,180 +1145,177 @@ def get_app(transport: str = "sse", enabled_apps: list[str] | None = None):
             "Dynamic tool filtering enabled for OAuth mode (JWT and Bearer tokens)"
         )
 
-    if transport == "sse":
-        mcp_app = mcp.sse_app()
-        starlette_lifespan = None
-    elif transport in ("http", "streamable-http"):
-        mcp_app = mcp.streamable_http_app()
+    mcp_app = mcp.streamable_http_app()
 
-        @asynccontextmanager
-        async def starlette_lifespan(app: Starlette):
-            # Set OAuth context for OAuth login routes (ADR-004)
-            if oauth_enabled:
-                # Prepare OAuth config from setup_oauth_config closure variables
-                mcp_server_url = os.getenv(
-                    "NEXTCLOUD_MCP_SERVER_URL", "http://localhost:8000"
-                )
-                nextcloud_resource_uri = os.getenv(
-                    "NEXTCLOUD_RESOURCE_URI", nextcloud_host
-                )
-                discovery_url = os.getenv(
-                    "OIDC_DISCOVERY_URL",
-                    f"{nextcloud_host}/.well-known/openid-configuration",
-                )
-                scopes = os.getenv("NEXTCLOUD_OIDC_SCOPES", "")
+    @asynccontextmanager
+    async def starlette_lifespan(app: Starlette):
+        # Set OAuth context for OAuth login routes (ADR-004)
+        if oauth_enabled:
+            # Prepare OAuth config from setup_oauth_config closure variables
+            mcp_server_url = os.getenv(
+                "NEXTCLOUD_MCP_SERVER_URL", "http://localhost:8000"
+            )
+            nextcloud_resource_uri = os.getenv("NEXTCLOUD_RESOURCE_URI", nextcloud_host)
+            discovery_url = os.getenv(
+                "OIDC_DISCOVERY_URL",
+                f"{nextcloud_host}/.well-known/openid-configuration",
+            )
+            scopes = os.getenv("NEXTCLOUD_OIDC_SCOPES", "")
 
-                oauth_context_dict = {
-                    "storage": refresh_token_storage,
-                    "oauth_client": oauth_client,
-                    "token_verifier": token_verifier,  # For querying IdP userinfo endpoint
-                    "config": {
-                        "mcp_server_url": mcp_server_url,
-                        "discovery_url": discovery_url,
-                        "client_id": client_id,  # From setup_oauth_config (DCR or static)
-                        "client_secret": client_secret,  # From setup_oauth_config (DCR or static)
-                        "scopes": scopes,
-                        "nextcloud_host": nextcloud_host,
-                        "nextcloud_resource_uri": nextcloud_resource_uri,
-                        "oauth_provider": oauth_provider,
-                    },
-                }
-                app.state.oauth_context = oauth_context_dict
+            oauth_context_dict = {
+                "storage": refresh_token_storage,
+                "oauth_client": oauth_client,
+                "token_verifier": token_verifier,  # For querying IdP userinfo endpoint
+                "config": {
+                    "mcp_server_url": mcp_server_url,
+                    "discovery_url": discovery_url,
+                    "client_id": client_id,  # From setup_oauth_config (DCR or static)
+                    "client_secret": client_secret,  # From setup_oauth_config (DCR or static)
+                    "scopes": scopes,
+                    "nextcloud_host": nextcloud_host,
+                    "nextcloud_resource_uri": nextcloud_resource_uri,
+                    "oauth_provider": oauth_provider,
+                },
+            }
+            app.state.oauth_context = oauth_context_dict
 
-                # Also set oauth_context on browser_app for session authentication
-                # browser_app is in the same function scope (defined later in create_app)
-                # We need to find it in the mounted routes
-                for route in app.routes:
-                    if isinstance(route, Mount) and route.path == "/app":
-                        route.app.state.oauth_context = oauth_context_dict
-                        logger.info(
-                            "OAuth context shared with browser_app for session auth"
-                        )
-                        break
-
-                logger.info(
-                    f"OAuth context initialized for login routes (client_id={client_id[:16]}...)"
-                )
-            else:
-                # BasicAuth mode - share storage with browser_app for webhook management
-                from nextcloud_mcp_server.auth.storage import RefreshTokenStorage
-
-                storage = RefreshTokenStorage.from_env()
-                await storage.initialize()
-
-                app.state.storage = storage
-
-                # Also share with browser_app for webhook routes
-                for route in app.routes:
-                    if isinstance(route, Mount) and route.path == "/app":
-                        route.app.state.storage = storage
-                        logger.info(
-                            "Storage shared with browser_app for webhook management"
-                        )
-                        break
-
-            # Start background vector sync tasks for BasicAuth mode (ADR-007)
-            # For streamable-http transport, FastMCP lifespan isn't automatically triggered
-            # so we manually start background tasks here if vector sync is enabled
-            import anyio as anyio_module
-
-            settings = get_settings()
-            if not oauth_enabled and settings.vector_sync_enabled:
-                logger.info("Starting background vector sync tasks for BasicAuth mode")
-
-                # Get username from environment
-                username = os.getenv("NEXTCLOUD_USERNAME")
-                if not username:
-                    raise ValueError(
-                        "NEXTCLOUD_USERNAME required for vector sync in BasicAuth mode"
+            # Also set oauth_context on browser_app for session authentication
+            # browser_app is in the same function scope (defined later in create_app)
+            # We need to find it in the mounted routes
+            for route in app.routes:
+                if isinstance(route, Mount) and route.path == "/app":
+                    route.app.state.oauth_context = oauth_context_dict
+                    logger.info(
+                        "OAuth context shared with browser_app for session auth"
                     )
+                    break
 
-                # Get Nextcloud client from MCP app context
-                # Create client since we're outside FastMCP lifespan
-                client = NextcloudClient.from_env()
+            logger.info(
+                f"OAuth context initialized for login routes (client_id={client_id[:16]}...)"
+            )
+        else:
+            # BasicAuth mode - share storage with browser_app for webhook management
+            from nextcloud_mcp_server.auth.storage import RefreshTokenStorage
 
-                # Initialize Qdrant collection before starting background tasks
-                logger.info("Initializing Qdrant collection...")
-                from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client
+            storage = RefreshTokenStorage.from_env()
+            await storage.initialize()
 
-                try:
-                    await get_qdrant_client()  # Triggers collection creation if needed
-                    logger.info("Qdrant collection ready")
-                except Exception as e:
-                    logger.error(f"Failed to initialize Qdrant collection: {e}")
-                    raise RuntimeError(
-                        f"Cannot start vector sync - Qdrant initialization failed: {e}"
-                    ) from e
+            app.state.storage = storage
 
-                # Initialize shared state
-                send_stream, receive_stream = anyio_module.create_memory_object_stream(
-                    max_buffer_size=settings.vector_sync_queue_max_size
+            # Also share with browser_app for webhook routes
+            for route in app.routes:
+                if isinstance(route, Mount) and route.path == "/app":
+                    route.app.state.storage = storage
+                    logger.info(
+                        "Storage shared with browser_app for webhook management"
+                    )
+                    break
+
+        # Start background vector sync tasks for BasicAuth mode (ADR-007)
+        # Scanner runs at server-level (once), not per-session
+        import anyio as anyio_module
+
+        settings = get_settings()
+        if not oauth_enabled and settings.vector_sync_enabled:
+            logger.info("Starting background vector sync tasks for BasicAuth mode")
+
+            # Get username from environment
+            username = os.getenv("NEXTCLOUD_USERNAME")
+            if not username:
+                raise ValueError(
+                    "NEXTCLOUD_USERNAME required for vector sync in BasicAuth mode"
                 )
-                shutdown_event = anyio_module.Event()
-                scanner_wake_event = anyio_module.Event()
 
-                # Store in app state for access from routes (ADR-007)
-                app.state.document_send_stream = send_stream
-                app.state.document_receive_stream = receive_stream
-                app.state.shutdown_event = shutdown_event
-                app.state.scanner_wake_event = scanner_wake_event
+            # Create client for vector sync (server-level, not per-session)
+            client = NextcloudClient.from_env()
 
-                # Also share with browser_app for /app route
-                for route in app.routes:
-                    if isinstance(route, Mount) and route.path == "/app":
-                        route.app.state.document_send_stream = send_stream
-                        route.app.state.document_receive_stream = receive_stream
-                        route.app.state.shutdown_event = shutdown_event
-                        route.app.state.scanner_wake_event = scanner_wake_event
-                        logger.info(
-                            "Vector sync state shared with browser_app for /app"
-                        )
-                        break
+            # Initialize Qdrant collection before starting background tasks
+            logger.info("Initializing Qdrant collection...")
+            from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client
 
-                # Start background tasks using anyio TaskGroup
-                async with anyio_module.create_task_group() as tg:
-                    # Start scanner task
+            try:
+                await get_qdrant_client()  # Triggers collection creation if needed
+                logger.info("Qdrant collection ready")
+            except Exception as e:
+                logger.error(f"Failed to initialize Qdrant collection: {e}")
+                raise RuntimeError(
+                    f"Cannot start vector sync - Qdrant initialization failed: {e}"
+                ) from e
+
+            # Initialize shared state
+            send_stream, receive_stream = anyio_module.create_memory_object_stream(
+                max_buffer_size=settings.vector_sync_queue_max_size
+            )
+            shutdown_event = anyio_module.Event()
+            scanner_wake_event = anyio_module.Event()
+
+            # Store in app state for access from routes (ADR-007)
+            app.state.document_send_stream = send_stream
+            app.state.document_receive_stream = receive_stream
+            app.state.shutdown_event = shutdown_event
+            app.state.scanner_wake_event = scanner_wake_event
+
+            # Also store in module singleton for FastMCP session lifespans
+            _vector_sync_state.document_send_stream = send_stream
+            _vector_sync_state.document_receive_stream = receive_stream
+            _vector_sync_state.shutdown_event = shutdown_event
+            _vector_sync_state.scanner_wake_event = scanner_wake_event
+            logger.info("Vector sync state stored in module singleton")
+
+            # Also share with browser_app for /app route
+            for route in app.routes:
+                if isinstance(route, Mount) and route.path == "/app":
+                    route.app.state.document_send_stream = send_stream
+                    route.app.state.document_receive_stream = receive_stream
+                    route.app.state.shutdown_event = shutdown_event
+                    route.app.state.scanner_wake_event = scanner_wake_event
+                    logger.info("Vector sync state shared with browser_app for /app")
+                    break
+
+            # Start background tasks using anyio TaskGroup
+            async with anyio_module.create_task_group() as tg:
+                # Start scanner task
+                await tg.start(
+                    scanner_task,
+                    send_stream,
+                    shutdown_event,
+                    scanner_wake_event,
+                    client,
+                    username,
+                )
+
+                # Start processor pool (each gets a cloned receive stream)
+                for i in range(settings.vector_sync_processor_workers):
                     await tg.start(
-                        scanner_task,
-                        send_stream,
+                        processor_task,
+                        i,
+                        receive_stream.clone(),
                         shutdown_event,
-                        scanner_wake_event,
                         client,
                         username,
                     )
 
-                    # Start processor pool (each gets a cloned receive stream)
-                    for i in range(settings.vector_sync_processor_workers):
-                        await tg.start(
-                            processor_task,
-                            i,
-                            receive_stream.clone(),
-                            shutdown_event,
-                            client,
-                            username,
-                        )
+                logger.info(
+                    f"Background sync tasks started: 1 scanner + "
+                    f"{settings.vector_sync_processor_workers} processors"
+                )
 
-                    logger.info(
-                        f"Background sync tasks started: 1 scanner + "
-                        f"{settings.vector_sync_processor_workers} processors"
-                    )
-
-                    # Run MCP session manager and yield
-                    async with AsyncExitStack() as stack:
-                        await stack.enter_async_context(mcp.session_manager.run())
-                        try:
-                            yield
-                        finally:
-                            # Shutdown signal
-                            logger.info("Shutting down background sync tasks")
-                            shutdown_event.set()
-                            await client.close()
-                            # TaskGroup automatically cancels all tasks on exit
-            else:
-                # No vector sync - just run MCP session manager
+                # Run MCP session manager and yield
                 async with AsyncExitStack() as stack:
                     await stack.enter_async_context(mcp.session_manager.run())
-                    yield
+                    try:
+                        yield
+                    finally:
+                        # Shutdown signal
+                        logger.info("Shutting down background sync tasks")
+                        shutdown_event.set()
+                        await client.close()
+                        # TaskGroup automatically cancels all tasks on exit
+        else:
+            # No vector sync - just run MCP session manager
+            async with AsyncExitStack() as stack:
+                await stack.enter_async_context(mcp.session_manager.run())
+                yield
 
     # Health check endpoints for Kubernetes probes
     def health_live(request):

nextcloud_mcp_server/auth/viz_routes.py

@@ -22,6 +22,7 @@ from starlette.requests import Request
 from starlette.responses import HTMLResponse, JSONResponse
 
 from nextcloud_mcp_server.config import get_settings
+from nextcloud_mcp_server.observability.tracing import trace_operation
 from nextcloud_mcp_server.search import (
     BM25HybridSearchAlgorithm,
     SemanticSearchAlgorithm,
@@ -139,7 +140,10 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
             _get_authenticated_client_for_userinfo,
         )
 
-        async with await _get_authenticated_client_for_userinfo(request) as nc_client:  # noqa: F841
+        with trace_operation("vector_viz.get_auth_client"):
+            auth_client_ctx = await _get_authenticated_client_for_userinfo(request)
+
+        async with auth_client_ctx as nc_client:  # noqa: F841
             # Create search algorithm (no client needed - verification removed)
             if algorithm == "semantic":
                 search_algo = SemanticSearchAlgorithm(score_threshold=score_threshold)
@@ -159,24 +163,40 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
             all_results = []
             if doc_types is None or len(doc_types) == 0:
                 # Cross-app search - search all indexed types
-                unverified_results = await search_algo.search(
-                    query=query,
-                    user_id=username,
-                    limit=limit * 2,  # Buffer for verification filtering
-                    doc_type=None,  # Search all types
-                    score_threshold=score_threshold,
-                )
-                all_results.extend(unverified_results)
-            else:
-                # Search each document type and combine
-                for doc_type in doc_types:
+                with trace_operation(
+                    "vector_viz.search_execute",
+                    attributes={
+                        "search.algorithm": algorithm,
+                        "search.limit": limit * 2,
+                        "search.doc_type": "all",
+                    },
+                ):
                     unverified_results = await search_algo.search(
                         query=query,
                         user_id=username,
                         limit=limit * 2,  # Buffer for verification filtering
-                        doc_type=doc_type,
+                        doc_type=None,  # Search all types
                         score_threshold=score_threshold,
                     )
+                all_results.extend(unverified_results)
+            else:
+                # Search each document type and combine
+                for doc_type in doc_types:
+                    with trace_operation(
+                        "vector_viz.search_execute",
+                        attributes={
+                            "search.algorithm": algorithm,
+                            "search.limit": limit * 2,
+                            "search.doc_type": doc_type,
+                        },
+                    ):
+                        unverified_results = await search_algo.search(
+                            query=query,
+                            user_id=username,
+                            limit=limit * 2,  # Buffer for verification filtering
+                            doc_type=doc_type,
+                            score_threshold=score_threshold,
+                        )
                     all_results.extend(unverified_results)
                 # Sort by score before verification
                 all_results.sort(key=lambda r: r.score, reverse=True)
@@ -190,22 +210,26 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
         # Store original scores and normalize for visualization
         # (best result = 1.0, worst result = 0.0 within THIS result set)
         # This makes visual encoding meaningful regardless of RRF normalization
-        if search_results:
-            scores = [r.score for r in search_results]
-            min_score, max_score = min(scores), max(scores)
-            score_range = max_score - min_score if max_score > min_score else 1.0
+        with trace_operation(
+            "vector_viz.score_normalize",
+            attributes={"normalize.num_results": len(search_results)},
+        ):
+            if search_results:
+                scores = [r.score for r in search_results]
+                min_score, max_score = min(scores), max(scores)
+                score_range = max_score - min_score if max_score > min_score else 1.0
 
-            logger.info(
-                f"Normalizing scores for viz: original range [{min_score:.3f}, {max_score:.3f}] "
-                f"→ [0.0, 1.0]"
-            )
+                logger.info(
+                    f"Normalizing scores for viz: original range [{min_score:.3f}, {max_score:.3f}] "
+                    f"→ [0.0, 1.0]"
+                )
 
-            # Store original score and rescale to 0-1 for visualization
-            for r in search_results:
-                # Store original score before normalization
-                r.original_score = r.score
-                # Rescale for visual encoding
-                r.score = (r.score - min_score) / score_range
+                # Store original score and rescale to 0-1 for visualization
+                for r in search_results:
+                    # Store original score before normalization
+                    r.original_score = r.score
+                    # Rescale for visual encoding
+                    r.score = (r.score - min_score) / score_range
 
         if not search_results:
             return JSONResponse(
@@ -220,7 +244,9 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
 
         # Fetch vectors for specific matching chunks from Qdrant using batch retrieve
         vector_fetch_start = time.perf_counter()
-        qdrant_client = await get_qdrant_client()
+
+        with trace_operation("vector_viz.get_qdrant_client"):
+            qdrant_client = await get_qdrant_client()
 
         chunk_vectors_map = {}  # Map (doc_id, chunk_start, chunk_end) -> vector
 
@@ -231,12 +257,16 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
         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(),
-                ids=point_ids,
-                with_vectors=["dense"],
-                with_payload=["doc_id", "chunk_start_offset", "chunk_end_offset"],
-            )
+            with trace_operation(
+                "vector_viz.vector_retrieve",
+                attributes={"retrieve.num_points": len(point_ids)},
+            ):
+                points_response = await qdrant_client.retrieve(
+                    collection_name=settings.get_collection_name(),
+                    ids=point_ids,
+                    with_vectors=["dense"],
+                    with_payload=["doc_id", "chunk_start_offset", "chunk_end_offset"],
+                )
 
             # Build chunk_vectors_map from batch response
             for point in points_response:
@@ -367,9 +397,16 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
 
         import anyio
 
-        coords_3d, pca = await anyio.to_thread.run_sync(  # type: ignore[attr-defined]
-            lambda: _compute_pca(all_vectors_normalized)
-        )
+        with trace_operation(
+            "vector_viz.pca_compute",
+            attributes={
+                "pca.num_vectors": len(all_vectors_normalized),
+                "pca.embedding_dim": embedding_dim,
+            },
+        ):
+            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/cli.py

@@ -29,9 +29,9 @@ from .app import get_app
 @click.option(
     "--transport",
     "-t",
-    default="sse",
+    default="streamable-http",
     show_default=True,
-    type=click.Choice(["sse", "streamable-http", "http"]),
+    type=click.Choice(["streamable-http", "http"]),
     help="MCP transport protocol",
 )
 @click.option(

nextcloud_mcp_server/client/webdav.py

@@ -1398,7 +1398,7 @@ class WebDAVClient(BaseNextcloudClient):
         user_visible: bool = True,
         user_assignable: bool = True,
     ) -> dict[str, Any]:
-        """Create a system tag via OCS API.
+        """Create a system tag via WebDAV.
 
         Args:
             name: Name of the tag to create
@@ -1411,12 +1411,10 @@ class WebDAVClient(BaseNextcloudClient):
         Raises:
             HTTPStatusError: If tag creation fails (409 if already exists)
         """
+        # Use WebDAV POST with JSON body to create tag
         response = await self._client.post(
-            "/ocs/v2.php/apps/systemtags/api/v1/tags",
-            headers={
-                "OCS-APIRequest": "true",
-                "Content-Type": "application/json",
-            },
+            "/remote.php/dav/systemtags/",
+            headers={"Content-Type": "application/json"},
             json={
                 "name": name,
                 "userVisible": user_visible,
@@ -1425,15 +1423,21 @@ class WebDAVClient(BaseNextcloudClient):
         )
         response.raise_for_status()
 
-        # Parse OCS response
-        data = response.json()
-        ocs_data = data.get("ocs", {}).get("data", {})
+        # Extract tag ID from Content-Location header (e.g., /remote.php/dav/systemtags/42)
+        content_location = response.headers.get("Content-Location", "")
+        tag_id = None
+        if content_location:
+            # Extract the numeric ID from the path
+            try:
+                tag_id = int(content_location.rstrip("/").split("/")[-1])
+            except (ValueError, IndexError):
+                pass
 
         tag_info = {
-            "id": ocs_data.get("id"),
-            "name": ocs_data.get("name", name),
-            "userVisible": ocs_data.get("userVisible", user_visible),
-            "userAssignable": ocs_data.get("userAssignable", user_assignable),
+            "id": tag_id,
+            "name": name,
+            "userVisible": user_visible,
+            "userAssignable": user_assignable,
         }
 
         logger.info(f"Created tag '{name}' with ID {tag_info['id']}")

nextcloud_mcp_server/search/bm25_hybrid.py

@@ -9,6 +9,7 @@ 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.observability.tracing import trace_operation
 from nextcloud_mcp_server.search.algorithms import SearchAlgorithm, SearchResult
 from nextcloud_mcp_server.vector.placeholder import get_placeholder_filter
 from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client
@@ -99,15 +100,19 @@ class BM25HybridSearchAlgorithm(SearchAlgorithm):
         )
 
         # Generate dense embedding for semantic search
-        embedding_service = get_embedding_service()
-        dense_embedding = await embedding_service.embed(query)
+        with trace_operation("search.get_embedding_service"):
+            embedding_service = get_embedding_service()
+        with trace_operation("search.dense_embedding"):
+            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 = await bm25_service.encode_async(query)
+        with trace_operation("search.get_bm25_service"):
+            bm25_service = get_bm25_service()
+        with trace_operation("search.sparse_embedding_bm25"):
+            sparse_embedding = await bm25_service.encode_async(query)
         logger.debug(
             f"Generated sparse embedding "
             f"({len(sparse_embedding['indices'])} non-zero terms)"
@@ -134,38 +139,44 @@ class BM25HybridSearchAlgorithm(SearchAlgorithm):
         query_filter = Filter(must=filter_conditions)
 
         # Execute hybrid search with Qdrant native RRF fusion
-        qdrant_client = await get_qdrant_client()
+        with trace_operation("search.get_qdrant_client"):
+            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"],
+            with trace_operation(
+                "search.qdrant_query",
+                attributes={"query.limit": limit * 2, "query.fusion": self.fusion_name},
+            ):
+                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,
                         ),
-                        using="sparse",
-                        limit=limit * 2,  # Get extra for deduplication
-                        filter=query_filter,
-                    ),
-                ],
-                # Fusion query (RRF or DBSF based on initialization)
-                query=models.FusionQuery(fusion=self.fusion),
-                limit=limit * 2,  # Get extra for deduplication
-                score_threshold=score_threshold,
-                with_payload=True,
-                with_vectors=False,  # Don't return vectors to save bandwidth
-            )
+                        # 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,
+                        ),
+                    ],
+                    # Fusion query (RRF or DBSF based on initialization)
+                    query=models.FusionQuery(fusion=self.fusion),
+                    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")
@@ -185,47 +196,51 @@ class BM25HybridSearchAlgorithm(SearchAlgorithm):
 
         # Deduplicate by (doc_id, doc_type, chunk_start, chunk_end)
         # This allows multiple chunks from same doc, but removes duplicate chunks
-        seen_chunks = set()
-        results = []
+        with trace_operation(
+            "search.deduplicate",
+            attributes={"dedupe.num_points": len(search_response.points)},
+        ):
+            seen_chunks = set()
+            results = []
 
-        for result in search_response.points:
-            # doc_id can be int (notes) or str (files - file paths)
-            doc_id = result.payload["doc_id"]
-            doc_type = result.payload.get("doc_type", "note")
-            chunk_start = result.payload.get("chunk_start_offset")
-            chunk_end = result.payload.get("chunk_end_offset")
-            chunk_key = (doc_id, doc_type, chunk_start, chunk_end)
+            for result in search_response.points:
+                # doc_id can be int (notes) or str (files - file paths)
+                doc_id = result.payload["doc_id"]
+                doc_type = result.payload.get("doc_type", "note")
+                chunk_start = result.payload.get("chunk_start_offset")
+                chunk_end = result.payload.get("chunk_end_offset")
+                chunk_key = (doc_id, doc_type, chunk_start, chunk_end)
 
-            # Skip if we've already seen this exact chunk
-            if chunk_key in seen_chunks:
-                continue
+                # Skip if we've already seen this exact chunk
+                if chunk_key in seen_chunks:
+                    continue
 
-            seen_chunks.add(chunk_key)
+                seen_chunks.add(chunk_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,  # Fusion score (RRF or DBSF)
-                    metadata={
-                        "chunk_index": result.payload.get("chunk_index"),
-                        "total_chunks": result.payload.get("total_chunks"),
-                        "search_method": f"bm25_hybrid_{self.fusion_name}",
-                    },
-                    chunk_start_offset=result.payload.get("chunk_start_offset"),
-                    chunk_end_offset=result.payload.get("chunk_end_offset"),
-                    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
+                # 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,  # Fusion score (RRF or DBSF)
+                        metadata={
+                            "chunk_index": result.payload.get("chunk_index"),
+                            "total_chunks": result.payload.get("total_chunks"),
+                            "search_method": f"bm25_hybrid_{self.fusion_name}",
+                        },
+                        chunk_start_offset=result.payload.get("chunk_start_offset"),
+                        chunk_end_offset=result.payload.get("chunk_end_offset"),
+                        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
+                    )
                 )
-            )
 
-            if len(results) >= limit:
-                break
+                if len(results) >= limit:
+                    break
 
         logger.info(f"Returning {len(results)} unverified results after deduplication")
         if results:

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "nextcloud-mcp-server"
-version = "0.48.0"
+version = "0.48.2"
 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"}

tests/conftest.py

@@ -9,7 +9,6 @@ import pytest
 from httpx import HTTPStatusError
 from mcp import ClientSession
 from mcp.client.session import RequestContext
-from mcp.client.sse import sse_client
 from mcp.client.streamable_http import streamablehttp_client
 from mcp.types import ElicitRequestParams, ElicitResult, ErrorData
 
@@ -172,51 +171,6 @@ async def create_mcp_client_session(
     logger.debug(f"{client_name} client session cleaned up successfully")
 
 
-async def create_mcp_client_session_sse(
-    url: str,
-    token: str | None = None,
-    client_name: str = "MCP",
-    elicitation_callback: Any = None,
-) -> AsyncGenerator[ClientSession, Any]:
-    """
-    Factory function to create an MCP client session using SSE transport.
-
-    Similar to create_mcp_client_session but uses SSE transport instead of streamable-http.
-    Uses native async context managers to ensure correct LIFO cleanup order.
-
-    Args:
-        url: MCP server URL (e.g., "http://localhost:8000/sse")
-        token: Optional OAuth access token for Bearer authentication
-        client_name: Client name for logging (e.g., "Basic MCP (SSE)")
-        elicitation_callback: Optional callback for handling elicitation requests
-
-    Yields:
-        Initialized MCP ClientSession
-
-    Note:
-        SSE transport is being deprecated in favor of streamable-http.
-        This function exists for compatibility testing only.
-    """
-    logger.info(f"Creating SSE client for {client_name}")
-
-    # Prepare headers with OAuth token if provided
-    headers = {"Authorization": f"Bearer {token}"} if token else None
-
-    # Use native async with - Python ensures LIFO cleanup
-    # Cleanup order will be: ClientSession.__aexit__ -> sse_client.__aexit__
-    # Note: sse_client yields only (read_stream, write_stream), not 3 values like streamablehttp_client
-    async with sse_client(url, headers=headers) as (read_stream, write_stream):
-        async with ClientSession(
-            read_stream, write_stream, elicitation_callback=elicitation_callback
-        ) as session:
-            await session.initialize()
-            logger.info(f"{client_name} client session initialized successfully")
-            yield session
-
-    # Cleanup happens automatically in LIFO order - no exception suppression needed
-    logger.debug(f"{client_name} client session cleaned up successfully")
-
-
 @pytest.fixture(scope="session")
 async def nc_client(anyio_backend) -> AsyncGenerator[NextcloudClient, Any]:
     """
@@ -255,18 +209,10 @@ async def nc_client(anyio_backend) -> AsyncGenerator[NextcloudClient, Any]:
 @pytest.fixture(scope="session")
 async def nc_mcp_client(anyio_backend) -> AsyncGenerator[ClientSession, Any]:
     """
-    Fixture to create an MCP client session for integration tests using SSE transport.
+    Fixture to create an MCP client session for integration tests using streamable-http.
 
     Uses anyio pytest plugin for proper async fixture handling.
-
-    Note: SSE transport is being deprecated. This fixture uses SSE for compatibility testing.
     """
-
-    # async for session in create_mcp_client_session_sse(
-    # url="http://localhost:8000/sse", client_name="Basic MCP (SSE)"
-    # ):
-    # yield session
-
     async for session in create_mcp_client_session(
         url="http://localhost:8000/mcp",
         client_name="Basic MCP (HTTP)",

tests/integration/test_rag_openai.py

@@ -42,6 +42,34 @@ logger = logging.getLogger(__name__)
 # Default path to the Nextcloud User Manual PDF
 DEFAULT_MANUAL_PATH = "Nextcloud Manual.pdf"
 
+
+async def llm_judge(
+    provider: "OpenAIProvider",
+    ground_truth: str,
+    system_output: str,
+) -> bool:
+    """Use LLM to judge if system output aligns with ground truth.
+
+    Args:
+        provider: OpenAI provider with generation capability
+        ground_truth: The expected/reference answer
+        system_output: The system's actual output to evaluate
+
+    Returns:
+        True if output aligns with ground truth, False otherwise
+    """
+    prompt = f"""GROUND TRUTH: {ground_truth}
+
+SYSTEM OUTPUT: {system_output}
+
+Does the system output contain the key facts from the ground truth?
+
+Answer: TRUE or FALSE"""
+
+    response = await provider.generate(prompt, max_tokens=10)
+    return "TRUE" in response.upper()
+
+
 # Skip all tests if OpenAI API key not configured
 pytestmark = [
     pytest.mark.integration,
@@ -218,7 +246,7 @@ async def test_openai_embeddings_work(openai_provider: OpenAIProvider):
 
 
 async def test_semantic_search_retrieval(
-    nc_mcp_client, ground_truth_qa, indexed_manual_pdf
+    nc_mcp_client, ground_truth_qa, indexed_manual_pdf, openai_generation_provider
 ):
     """Test that semantic search retrieves relevant documents from the manual.
 
@@ -228,7 +256,6 @@ async def test_semantic_search_retrieval(
     # Use first query from ground truth
     test_case = ground_truth_qa[0]  # 2FA question
     query = test_case["query"]
-    expected_topics = test_case["expected_topics"]
 
     # Perform semantic search via MCP tool
     result = await nc_mcp_client.call_tool(
@@ -248,16 +275,21 @@ async def test_semantic_search_retrieval(
     assert data["total_found"] > 0, f"No results for query: {query}"
     assert len(data["results"]) > 0
 
-    # Check that at least one result contains expected topic keywords
-    all_excerpts = " ".join([r["excerpt"].lower() for r in data["results"]])
-    topic_found = any(topic.lower() in all_excerpts for topic in expected_topics)
-    assert topic_found, (
-        f"Expected topics {expected_topics} not found in results for query: {query}"
+    # Use LLM judge to evaluate if excerpts are relevant to ground truth
+    all_excerpts = " ".join([r["excerpt"] for r in data["results"]])
+    is_relevant = await llm_judge(
+        openai_generation_provider,
+        test_case["ground_truth"],
+        all_excerpts,
     )
+    assert is_relevant, f"LLM judge: excerpts not relevant to query: {query}"
 
 
 async def test_semantic_search_answer_with_sampling(
-    nc_mcp_client_with_sampling, ground_truth_qa, indexed_manual_pdf
+    nc_mcp_client_with_sampling,
+    ground_truth_qa,
+    indexed_manual_pdf,
+    openai_generation_provider,
 ):
     """Test semantic search with MCP sampling for answer generation.
 
@@ -314,12 +346,13 @@ async def test_semantic_search_answer_with_sampling(
         assert data["generated_answer"] is not None
         assert len(data["generated_answer"]) > 50  # Non-trivial answer
 
-        # Check answer contains relevant content
-        answer_lower = data["generated_answer"].lower()
-        assert any(
-            keyword in answer_lower
-            for keyword in ["two-factor", "2fa", "authentication", "password"]
-        ), f"Answer doesn't seem relevant to query: {data['generated_answer'][:200]}"
+        # Use LLM judge to evaluate answer relevance
+        is_relevant = await llm_judge(
+            openai_generation_provider,
+            test_case["ground_truth"],
+            data["generated_answer"],
+        )
+        assert is_relevant, f"LLM judge: answer not relevant to query: {query}"
 
 
 @pytest.mark.parametrize(

uv.lock

@@ -1936,7 +1936,7 @@ wheels = [
 
 [[package]]
 name = "nextcloud-mcp-server"
-version = "0.48.0"
+version = "0.48.2"
 source = { editable = "." }
 dependencies = [
     { name = "aiosqlite" },