Update README

.github/workflows/rag-evaluation.yml

@@ -3,10 +3,6 @@ name: RAG Evaluation
 on:
   workflow_dispatch:
     inputs:
-      manual_path:
-        description: 'Path to Nextcloud User Manual PDF in Nextcloud'
-        required: false
-        default: 'Nextcloud Manual.pdf'
       embedding_model:
         description: 'OpenAI embedding model'
         required: false
@@ -19,15 +15,40 @@ on:
 jobs:
   rag-evaluation:
     runs-on: ubuntu-latest
-    timeout-minutes: 30
-    permissions:
-      models: read
+    timeout-minutes: 45
 
     steps:
       - uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6.0.0
         with:
           submodules: 'true'
 
+      - name: Clone Nextcloud documentation
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6.0.0
+        with:
+          repository: 'nextcloud/documentation'
+          path: 'nextcloud-docs'
+
+      - name: Install Sphinx and LaTeX dependencies
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y \
+            python3-sphinx \
+            python3-pip \
+            latexmk \
+            texlive-latex-recommended \
+            texlive-latex-extra \
+            texlive-fonts-recommended \
+            texlive-fonts-extra
+
+      - name: Build User Manual PDF
+        run: |
+          cd nextcloud-docs/user_manual
+          pip3 install -r ../requirements.txt
+          make latexpdf
+          ls -la _build/latex/
+          cp _build/latex/NextcloudUserManual.pdf ../../Nextcloud_User_Manual.pdf
+          echo "PDF built successfully"
+
       ###### Required to build OIDC App ######
       - name: Set up php 8.4
         uses: shivammathur/setup-php@bf6b4fbd49ca58e4608c9c89fba0b8d90bd2a39f # v2
@@ -49,7 +70,7 @@ jobs:
         env:
           # Override MCP container environment for OpenAI + vector sync
           VECTOR_SYNC_ENABLED: "true"
-          VECTOR_SYNC_SCAN_INTERVAL: "5"
+          VECTOR_SYNC_SCAN_INTERVAL: "30"
           OPENAI_API_KEY: ${{ secrets.GITHUB_TOKEN }}
           OPENAI_BASE_URL: "https://models.github.ai/inference"
           OPENAI_EMBEDDING_MODEL: ${{ inputs.embedding_model }}
@@ -79,7 +100,7 @@ jobs:
           echo "Waiting for MCP server..."
           max_attempts=30
           attempt=0
-          until curl -o /dev/null -s -w "%{http_code}\n" http://localhost:8000/health/live | grep -q "200"; do
+          until curl -o /dev/null -s -w "%{http_code}\n" http://localhost:8000/health | grep -q "200"; do
             attempt=$((attempt + 1))
             if [ $attempt -ge $max_attempts ]; then
               echo "MCP server did not become ready in time."
@@ -90,12 +111,149 @@ jobs:
           done
           echo "MCP server is ready."
 
+      - name: Upload User Manual PDF to Nextcloud
+        run: |
+          echo "Uploading Nextcloud_User_Manual.pdf to Nextcloud..."
+          HTTP_CODE=$(curl -s -o /dev/null -w "%{http_code}" -u admin:admin \
+            -X PUT \
+            -T Nextcloud_User_Manual.pdf \
+            "http://localhost:8080/remote.php/dav/files/admin/Nextcloud_User_Manual.pdf")
+
+          if [ "$HTTP_CODE" = "201" ] || [ "$HTTP_CODE" = "204" ]; then
+            echo "PDF uploaded successfully (HTTP $HTTP_CODE)"
+          else
+            echo "Failed to upload PDF (HTTP $HTTP_CODE)"
+            exit 1
+          fi
+
+      - name: Create vector-index tag
+        id: create_tag
+        run: |
+          # Create the tag using OCS API
+          echo "Creating vector-index tag..."
+          RESPONSE=$(curl -s -u admin:admin \
+            -X POST \
+            -H 'Content-Type: application/json' \
+            -H 'OCS-APIRequest: true' \
+            -d '{"name":"vector-index","userVisible":true,"userAssignable":true}' \
+            "http://localhost:8080/ocs/v2.php/apps/systemtags/api/v1/tags")
+
+          echo "Create tag response: $RESPONSE"
+
+          # Get tag ID from response or lookup
+          TAG_ID=$(echo "$RESPONSE" | grep -oP '(?<="id":)[0-9]+' | head -1 || echo "")
+
+          if [ -z "$TAG_ID" ]; then
+            echo "Tag may already exist, looking it up..."
+            TAG_ID=$(curl -s -u admin:admin \
+              -X PROPFIND \
+              -H 'Content-Type: application/xml' \
+              -d '<?xml version="1.0"?><d:propfind xmlns:d="DAV:" xmlns:oc="http://owncloud.org/ns"><d:prop><oc:id/><oc:display-name/></d:prop></d:propfind>' \
+              http://localhost:8080/remote.php/dav/systemtags/ \
+              | grep -B2 "vector-index" | grep -oP '(?<=<oc:id>)[0-9]+(?=</oc:id>)' | head -1 || echo "")
+          fi
+
+          if [ -z "$TAG_ID" ]; then
+            echo "ERROR: Could not create or find vector-index tag"
+            exit 1
+          fi
+
+          echo "Tag ID: $TAG_ID"
+          echo "tag_id=$TAG_ID" >> $GITHUB_OUTPUT
+
+      - name: Get file ID of uploaded PDF
+        id: get_file_id
+        run: |
+          echo "Getting file ID for Nextcloud_User_Manual.pdf..."
+
+          # Get file ID using PROPFIND
+          FILE_ID=$(curl -s -u admin:admin \
+            -X PROPFIND \
+            -H 'Content-Type: application/xml' \
+            -d '<?xml version="1.0"?><d:propfind xmlns:d="DAV:" xmlns:oc="http://owncloud.org/ns"><d:prop><oc:fileid/></d:prop></d:propfind>' \
+            "http://localhost:8080/remote.php/dav/files/admin/Nextcloud_User_Manual.pdf" \
+            | grep -oP '(?<=<oc:fileid>)[0-9]+(?=</oc:fileid>)' || echo "")
+
+          if [ -z "$FILE_ID" ]; then
+            echo "ERROR: Could not find file ID"
+            exit 1
+          fi
+
+          echo "Found file ID: $FILE_ID"
+          echo "file_id=$FILE_ID" >> $GITHUB_OUTPUT
+
+      - name: Tag file with vector-index
+        env:
+          FILE_ID: ${{ steps.get_file_id.outputs.file_id }}
+          TAG_ID: ${{ steps.create_tag.outputs.tag_id }}
+        run: |
+          echo "Tagging file $FILE_ID with tag $TAG_ID..."
+
+          HTTP_CODE=$(curl -s -o /dev/null -w "%{http_code}" -u admin:admin \
+            -X PUT \
+            -H 'Content-Type: application/json' \
+            -H 'Content-Length: 0' \
+            "http://localhost:8080/remote.php/dav/systemtags-relations/files/$FILE_ID/$TAG_ID")
+
+          if [ "$HTTP_CODE" = "201" ] || [ "$HTTP_CODE" = "409" ]; then
+            echo "File tagged successfully (HTTP $HTTP_CODE)"
+          else
+            echo "Failed to tag file (HTTP $HTTP_CODE)"
+            exit 1
+          fi
+
+      - name: Wait for vector sync to complete indexing
+        env:
+          NEXTCLOUD_HOST: "http://localhost:8080"
+          NEXTCLOUD_USERNAME: "admin"
+          NEXTCLOUD_PASSWORD: "admin"
+        run: |
+          echo "Waiting for vector sync to index the manual..."
+          max_attempts=60
+          attempt=0
+
+          # Wait for initial scan to pick up the file
+          sleep 10
+
+          while [ $attempt -lt $max_attempts ]; do
+            attempt=$((attempt + 1))
+
+            # Check vector sync status via MCP
+            STATUS=$(curl -s http://localhost:8000/health || echo "{}")
+            echo "Attempt $attempt/$max_attempts: $STATUS"
+
+            # Also check indexed count via semantic search
+            # If we get results, indexing is done
+            RESULT=$(curl -s -X POST http://localhost:8000/mcp \
+              -H "Content-Type: application/json" \
+              -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"nc_get_vector_sync_status","arguments":{}}}' \
+              2>/dev/null || echo "{}")
+
+            echo "Vector sync status: $RESULT"
+
+            # Check if pending is 0 and indexed > 0
+            INDEXED=$(echo "$RESULT" | jq -r '.result.structuredContent.indexed // 0' 2>/dev/null || echo "0")
+            PENDING=$(echo "$RESULT" | jq -r '.result.structuredContent.pending // 1' 2>/dev/null || echo "1")
+
+            echo "Indexed: $INDEXED, Pending: $PENDING"
+
+            if [ "$INDEXED" -gt "0" ] && [ "$PENDING" -eq "0" ]; then
+              echo "Indexing complete! $INDEXED documents indexed."
+              break
+            fi
+
+            sleep 10
+          done
+
+          if [ $attempt -ge $max_attempts ]; then
+            echo "WARNING: Indexing may not be complete, proceeding anyway..."
+          fi
+
       - name: Run RAG evaluation tests
         env:
           NEXTCLOUD_HOST: "http://localhost:8080"
           NEXTCLOUD_USERNAME: "admin"
           NEXTCLOUD_PASSWORD: "admin"
-          RAG_MANUAL_PATH: ${{ inputs.manual_path }}
           OPENAI_API_KEY: ${{ secrets.GITHUB_TOKEN }}
           OPENAI_BASE_URL: "https://models.github.ai/inference"
           OPENAI_EMBEDDING_MODEL: ${{ inputs.embedding_model }}

CHANGELOG.md

@@ -1,26 +1,3 @@
-## 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
-
-- Add tag management methods to WebDAV client
-
 ## v0.47.0 (2025-11-23)
 
 ### Feat

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: 0.48.2
-appVersion: "0.48.2"
+version: 0.47.0
+appVersion: "0.47.0"
 keywords:
   - nextcloud
   - mcp

nextcloud_mcp_server/app.py

@@ -243,25 +243,6 @@ 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."""
@@ -574,15 +555,15 @@ async def load_oauth_client_credentials(
 @asynccontextmanager
 async def app_lifespan_basic(server: FastMCP) -> AsyncIterator[AppContext]:
     """
-    Manage application lifecycle for BasicAuth mode (FastMCP session lifespan).
+    Manage application lifecycle for BasicAuth mode.
 
     Creates a single Nextcloud client with basic authentication
-    that is shared across all requests within a session.
+    that is shared across all requests.
 
-    Note: Background tasks (scanner, processor) are started at server level
-    in starlette_lifespan, not here. This lifespan runs per-session.
+    If vector sync is enabled (VECTOR_SYNC_ENABLED=true), also starts
+    background tasks for automatic document indexing (ADR-007).
     """
-    logger.info("Starting MCP session in BasicAuth mode")
+    logger.info("Starting MCP server in BasicAuth mode")
     logger.info("Creating Nextcloud client with BasicAuth")
 
     client = NextcloudClient.from_env()
@@ -598,20 +579,91 @@ async def app_lifespan_basic(server: FastMCP) -> AsyncIterator[AppContext]:
     # Initialize document processors
     initialize_document_processors()
 
-    # 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,
+    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
         )
-    finally:
-        logger.info("Shutting down BasicAuth session")
-        await client.close()
+        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()
 
 
 async def setup_oauth_config():
@@ -927,7 +979,7 @@ async def setup_oauth_config():
     )
 
 
-def get_app(transport: str = "streamable-http", enabled_apps: list[str] | None = None):
+def get_app(transport: str = "sse", enabled_apps: list[str] | None = None):
     # Initialize observability (logging will be configured by uvicorn)
     settings = get_settings()
 
@@ -1145,177 +1197,180 @@ def get_app(transport: str = "streamable-http", enabled_apps: list[str] | None =
             "Dynamic tool filtering enabled for OAuth mode (JWT and Bearer tokens)"
         )
 
-    mcp_app = mcp.streamable_http_app()
+    if transport == "sse":
+        mcp_app = mcp.sse_app()
+        starlette_lifespan = None
+    elif transport in ("http", "streamable-http"):
+        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", "")
-
-            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)
-        # 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"
+        @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"
                 )
-
-            # Create client for vector sync (server-level, not per-session)
-            client = NextcloudClient.from_env()
-
-            # 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_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,
+                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", "")
 
-                # Start processor pool (each gets a cloned receive stream)
-                for i in range(settings.vector_sync_processor_workers):
+                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"
+                    )
+
+                # Get Nextcloud client from MCP app context
+                # Create client since we're outside FastMCP lifespan
+                client = NextcloudClient.from_env()
+
+                # 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_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 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(
-                        processor_task,
-                        i,
-                        receive_stream.clone(),
+                        scanner_task,
+                        send_stream,
                         shutdown_event,
+                        scanner_wake_event,
                         client,
                         username,
                     )
 
-                logger.info(
-                    f"Background sync tasks started: 1 scanner + "
-                    f"{settings.vector_sync_processor_workers} processors"
-                )
+                    # 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,
+                        )
 
-                # Run MCP session manager and yield
+                    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
                 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
-            async with AsyncExitStack() as stack:
-                await stack.enter_async_context(mcp.session_manager.run())
-                yield
+                    yield
 
     # Health check endpoints for Kubernetes probes
     def health_live(request):

nextcloud_mcp_server/auth/viz_routes.py

@@ -22,7 +22,6 @@ 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,
@@ -140,10 +139,7 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
             _get_authenticated_client_for_userinfo,
         )
 
-        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
+        async with await _get_authenticated_client_for_userinfo(request) as nc_client:  # noqa: F841
             # Create search algorithm (no client needed - verification removed)
             if algorithm == "semantic":
                 search_algo = SemanticSearchAlgorithm(score_threshold=score_threshold)
@@ -163,40 +159,24 @@ 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
-                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=None,  # Search all types
-                        score_threshold=score_threshold,
-                    )
+                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": 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,
-                        )
+                    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)
@@ -210,26 +190,22 @@ 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
-        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
+        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(
@@ -244,9 +220,7 @@ 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()
-
-        with trace_operation("vector_viz.get_qdrant_client"):
-            qdrant_client = await get_qdrant_client()
+        qdrant_client = await get_qdrant_client()
 
         chunk_vectors_map = {}  # Map (doc_id, chunk_start, chunk_end) -> vector
 
@@ -257,16 +231,12 @@ 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)
-            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"],
-                )
+            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:
@@ -397,16 +367,9 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
 
         import anyio
 
-        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)
-            )
+        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="streamable-http",
+    default="sse",
     show_default=True,
-    type=click.Choice(["streamable-http", "http"]),
+    type=click.Choice(["sse", "streamable-http", "http"]),
     help="MCP transport protocol",
 )
 @click.option(

nextcloud_mcp_server/client/webdav.py

@@ -1295,237 +1295,3 @@ class WebDAVClient(BaseNextcloudClient):
 
         logger.debug(f"Found {len(files)} files with tag ID {tag_id}")
         return files
-
-    async def get_file_info(self, path: str) -> dict[str, Any] | None:
-        """Get file info including file ID via WebDAV PROPFIND.
-
-        Args:
-            path: Path to the file (relative to user's files directory)
-
-        Returns:
-            File info dictionary with id, name, size, content_type, etc.
-            Returns None if file not found.
-        """
-        webdav_path = f"{self._get_webdav_base_path()}/{path.lstrip('/')}"
-
-        propfind_body = """<?xml version="1.0"?>
-<d:propfind xmlns:d="DAV:" xmlns:oc="http://owncloud.org/ns">
-  <d:prop>
-    <oc:fileid/>
-    <d:displayname/>
-    <d:getcontentlength/>
-    <d:getcontenttype/>
-    <d:getlastmodified/>
-    <d:getetag/>
-    <d:resourcetype/>
-  </d:prop>
-</d:propfind>"""
-
-        try:
-            response = await self._client.request(
-                "PROPFIND",
-                webdav_path,
-                headers={"Depth": "0"},
-                content=propfind_body,
-            )
-            response.raise_for_status()
-        except HTTPStatusError as e:
-            if e.response.status_code == 404:
-                logger.debug(f"File not found: {path}")
-                return None
-            raise
-
-        # Parse XML response
-        root = ET.fromstring(response.content)
-        ns = {
-            "d": "DAV:",
-            "oc": "http://owncloud.org/ns",
-        }
-
-        response_elem = root.find("d:response", ns)
-        if response_elem is None:
-            return None
-
-        propstat = response_elem.find("d:propstat", ns)
-        if propstat is None:
-            return None
-
-        prop = propstat.find("d:prop", ns)
-        if prop is None:
-            return None
-
-        # Extract properties
-        fileid_elem = prop.find("oc:fileid", ns)
-        displayname_elem = prop.find("d:displayname", ns)
-        contentlength_elem = prop.find("d:getcontentlength", ns)
-        contenttype_elem = prop.find("d:getcontenttype", ns)
-        lastmodified_elem = prop.find("d:getlastmodified", ns)
-        etag_elem = prop.find("d:getetag", ns)
-        resourcetype_elem = prop.find("d:resourcetype", ns)
-
-        is_directory = (
-            resourcetype_elem is not None
-            and resourcetype_elem.find("d:collection", ns) is not None
-        )
-
-        file_info = {
-            "id": int(fileid_elem.text) if fileid_elem is not None else None,
-            "path": path,
-            "name": displayname_elem.text
-            if displayname_elem is not None
-            else path.split("/")[-1],
-            "size": int(contentlength_elem.text)
-            if contentlength_elem is not None and contentlength_elem.text
-            else 0,
-            "content_type": contenttype_elem.text
-            if contenttype_elem is not None
-            else "",
-            "last_modified": lastmodified_elem.text
-            if lastmodified_elem is not None
-            else None,
-            "etag": etag_elem.text.strip('"')
-            if etag_elem is not None and etag_elem.text
-            else None,
-            "is_directory": is_directory,
-        }
-
-        logger.debug(f"Got file info for '{path}': id={file_info['id']}")
-        return file_info
-
-    async def create_tag(
-        self,
-        name: str,
-        user_visible: bool = True,
-        user_assignable: bool = True,
-    ) -> dict[str, Any]:
-        """Create a system tag via WebDAV.
-
-        Args:
-            name: Name of the tag to create
-            user_visible: Whether the tag is visible to users
-            user_assignable: Whether users can assign this tag
-
-        Returns:
-            Tag dictionary with id, name, userVisible, userAssignable
-
-        Raises:
-            HTTPStatusError: If tag creation fails (409 if already exists)
-        """
-        # Use WebDAV POST with JSON body to create tag
-        response = await self._client.post(
-            "/remote.php/dav/systemtags/",
-            headers={"Content-Type": "application/json"},
-            json={
-                "name": name,
-                "userVisible": user_visible,
-                "userAssignable": user_assignable,
-            },
-        )
-        response.raise_for_status()
-
-        # 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": tag_id,
-            "name": name,
-            "userVisible": user_visible,
-            "userAssignable": user_assignable,
-        }
-
-        logger.info(f"Created tag '{name}' with ID {tag_info['id']}")
-        return tag_info
-
-    async def get_or_create_tag(
-        self,
-        name: str,
-        user_visible: bool = True,
-        user_assignable: bool = True,
-    ) -> dict[str, Any]:
-        """Get a tag by name, creating it if it doesn't exist.
-
-        Args:
-            name: Name of the tag
-            user_visible: Whether the tag is visible to users (for creation)
-            user_assignable: Whether users can assign this tag (for creation)
-
-        Returns:
-            Tag dictionary with id, name, userVisible, userAssignable
-        """
-        # First try to get existing tag
-        existing_tag = await self.get_tag_by_name(name)
-        if existing_tag:
-            logger.debug(f"Tag '{name}' already exists with ID {existing_tag['id']}")
-            return existing_tag
-
-        # Create new tag
-        try:
-            return await self.create_tag(name, user_visible, user_assignable)
-        except HTTPStatusError as e:
-            if e.response.status_code == 409:
-                # Tag was created between our check and creation, fetch it
-                existing_tag = await self.get_tag_by_name(name)
-                if existing_tag:
-                    return existing_tag
-            raise
-
-    async def assign_tag_to_file(self, file_id: int, tag_id: int) -> bool:
-        """Assign a system tag to a file.
-
-        Args:
-            file_id: Numeric file ID
-            tag_id: Numeric tag ID
-
-        Returns:
-            True if tag was assigned successfully (or already assigned)
-
-        Raises:
-            HTTPStatusError: If tag assignment fails
-        """
-        response = await self._client.request(
-            "PUT",
-            f"/remote.php/dav/systemtags-relations/files/{file_id}/{tag_id}",
-            headers={"Content-Length": "0"},
-            content=b"",
-        )
-
-        # 201 = Created (new assignment), 409 = Conflict (already assigned)
-        if response.status_code in (201, 409):
-            logger.info(f"Tagged file {file_id} with tag {tag_id}")
-            return True
-
-        response.raise_for_status()
-        return True
-
-    async def remove_tag_from_file(self, file_id: int, tag_id: int) -> bool:
-        """Remove a system tag from a file.
-
-        Args:
-            file_id: Numeric file ID
-            tag_id: Numeric tag ID
-
-        Returns:
-            True if tag was removed successfully (or wasn't assigned)
-
-        Raises:
-            HTTPStatusError: If tag removal fails
-        """
-        response = await self._client.request(
-            "DELETE",
-            f"/remote.php/dav/systemtags-relations/files/{file_id}/{tag_id}",
-        )
-
-        # 204 = No Content (removed), 404 = Not Found (wasn't assigned)
-        if response.status_code in (204, 404):
-            logger.info(f"Removed tag {tag_id} from file {file_id}")
-            return True
-
-        response.raise_for_status()
-        return True

nextcloud_mcp_server/search/bm25_hybrid.py

@@ -9,7 +9,6 @@ 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
@@ -100,19 +99,15 @@ class BM25HybridSearchAlgorithm(SearchAlgorithm):
         )
 
         # Generate dense embedding for semantic search
-        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)
+        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
-        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)
+        bm25_service = get_bm25_service()
+        sparse_embedding = await bm25_service.encode_async(query)
         logger.debug(
             f"Generated sparse embedding "
             f"({len(sparse_embedding['indices'])} non-zero terms)"
@@ -139,44 +134,38 @@ class BM25HybridSearchAlgorithm(SearchAlgorithm):
         query_filter = Filter(must=filter_conditions)
 
         # Execute hybrid search with Qdrant native RRF fusion
-        with trace_operation("search.get_qdrant_client"):
-            qdrant_client = await 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
-            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,
+            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"],
                         ),
-                        # 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
-                )
+                        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")
@@ -196,51 +185,47 @@ class BM25HybridSearchAlgorithm(SearchAlgorithm):
 
         # Deduplicate by (doc_id, doc_type, chunk_start, chunk_end)
         # This allows multiple chunks from same doc, but removes duplicate chunks
-        with trace_operation(
-            "search.deduplicate",
-            attributes={"dedupe.num_points": len(search_response.points)},
-        ):
-            seen_chunks = set()
-            results = []
+        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 = "0.48.2"
+version = "0.47.0"
 description = "Model Context Protocol (MCP) server for Nextcloud integration - enables AI assistants to interact with Nextcloud data"
 authors = [
     {name = "Chris Coutinho", email = "chris@coutinho.io"}

tests/conftest.py

@@ -9,6 +9,7 @@ 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
 
@@ -171,6 +172,51 @@ 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]:
     """
@@ -209,10 +255,18 @@ 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 streamable-http.
+    Fixture to create an MCP client session for integration tests using SSE transport.
 
     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

@@ -10,7 +10,6 @@ Environment Variables:
     OPENAI_BASE_URL: Base URL override (e.g., "https://models.github.ai/inference")
     OPENAI_EMBEDDING_MODEL: Embedding model (default: "text-embedding-3-small")
     OPENAI_GENERATION_MODEL: Generation model for sampling (default: "gpt-4o-mini")
-    RAG_MANUAL_PATH: Path to manual PDF in Nextcloud (default: "Nextcloud_User_Manual.pdf")
 
 For GitHub CI, set:
     OPENAI_API_KEY: ${{ secrets.GITHUB_TOKEN }}
@@ -19,17 +18,15 @@ For GitHub CI, set:
     OPENAI_GENERATION_MODEL: openai/gpt-4o-mini
 
 Prerequisites:
-    - Nextcloud User Manual PDF uploaded to Nextcloud
+    - Nextcloud User Manual indexed in Qdrant (via vector sync)
     - VECTOR_SYNC_ENABLED=true on the MCP server
 """
 
 import json
-import logging
 import os
 from pathlib import Path
 from typing import Any, AsyncGenerator
 
-import anyio
 import pytest
 from mcp import ClientSession
 
@@ -37,39 +34,6 @@ from nextcloud_mcp_server.providers.openai import OpenAIProvider
 from tests.conftest import create_mcp_client_session
 from tests.integration.sampling_support import create_openai_sampling_callback
 
-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,
@@ -94,86 +58,6 @@ def ground_truth_qa():
         return json.load(f)
 
 
-@pytest.fixture(scope="module")
-async def indexed_manual_pdf(nc_client, nc_mcp_client):
-    """Ensure the Nextcloud User Manual PDF is tagged and indexed for vector search.
-
-    This fixture:
-    1. Gets file info for the manual PDF
-    2. Creates/gets the 'vector-index' tag
-    3. Assigns the tag to the file
-    4. Waits for vector sync to complete indexing
-
-    Environment Variables:
-        RAG_MANUAL_PATH: Path to manual PDF in Nextcloud (default: Nextcloud Manual.pdf)
-    """
-    manual_path = os.getenv("RAG_MANUAL_PATH", DEFAULT_MANUAL_PATH)
-
-    logger.info(f"Setting up indexed manual PDF: {manual_path}")
-
-    # Get file info to verify file exists and get file ID
-    file_info = await nc_client.webdav.get_file_info(manual_path)
-    if not file_info:
-        pytest.skip(f"Manual PDF not found at '{manual_path}'")
-
-    file_id = file_info["id"]
-    logger.info(f"Found manual PDF: {manual_path} (file_id={file_id})")
-
-    # Create or get the vector-index tag
-    tag = await nc_client.webdav.get_or_create_tag("vector-index")
-    tag_id = tag["id"]
-    logger.info(f"Using tag 'vector-index' (tag_id={tag_id})")
-
-    # Assign tag to file
-    await nc_client.webdav.assign_tag_to_file(file_id, tag_id)
-    logger.info(f"Tagged file {file_id} with vector-index tag")
-
-    # Wait for vector sync to complete indexing
-    max_attempts = 60
-    poll_interval = 10
-
-    logger.info("Waiting for vector sync to index the manual...")
-
-    for attempt in range(1, max_attempts + 1):
-        try:
-            # Call the MCP tool via the existing client session
-            result = await nc_mcp_client.call_tool(
-                "nc_get_vector_sync_status",
-                arguments={},
-            )
-
-            if not result.isError:
-                content = result.structuredContent or {}
-                indexed = content.get("indexed_count", 0)
-                pending = content.get("pending_count", 1)
-
-                logger.info(
-                    f"Attempt {attempt}/{max_attempts}: "
-                    f"indexed={indexed}, pending={pending}"
-                )
-
-                if indexed > 0 and pending == 0:
-                    logger.info(
-                        f"Vector indexing complete: {indexed} documents indexed"
-                    )
-                    break
-        except Exception as e:
-            logger.warning(f"Attempt {attempt}: Error checking status: {e}")
-
-        if attempt < max_attempts:
-            await anyio.sleep(poll_interval)
-    else:
-        logger.warning(
-            f"Vector indexing may not be complete after {max_attempts} attempts"
-        )
-
-    yield {
-        "path": manual_path,
-        "file_id": file_id,
-        "tag_id": tag_id,
-    }
-
-
 @pytest.fixture(scope="module")
 async def openai_provider():
     """OpenAI provider configured from environment (embeddings only)."""
@@ -245,9 +129,7 @@ async def test_openai_embeddings_work(openai_provider: OpenAIProvider):
     assert len(embedding) in [1536, 3072]
 
 
-async def test_semantic_search_retrieval(
-    nc_mcp_client, ground_truth_qa, indexed_manual_pdf, openai_generation_provider
-):
+async def test_semantic_search_retrieval(nc_mcp_client, ground_truth_qa):
     """Test that semantic search retrieves relevant documents from the manual.
 
     This tests the retrieval component of RAG - ensuring that queries
@@ -256,6 +138,7 @@ 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(
@@ -275,21 +158,16 @@ async def test_semantic_search_retrieval(
     assert data["total_found"] > 0, f"No results for query: {query}"
     assert len(data["results"]) > 0
 
-    # 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,
+    # 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}"
     )
-    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,
-    openai_generation_provider,
+    nc_mcp_client_with_sampling, ground_truth_qa
 ):
     """Test semantic search with MCP sampling for answer generation.
 
@@ -346,13 +224,12 @@ async def test_semantic_search_answer_with_sampling(
         assert data["generated_answer"] is not None
         assert len(data["generated_answer"]) > 50  # Non-trivial answer
 
-        # 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}"
+        # 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]}"
 
 
 @pytest.mark.parametrize(
@@ -366,7 +243,7 @@ async def test_semantic_search_answer_with_sampling(
     ],
 )
 async def test_retrieval_quality_all_queries(
-    nc_mcp_client, ground_truth_qa, indexed_manual_pdf, qa_index, min_expected_results
+    nc_mcp_client, ground_truth_qa, qa_index, min_expected_results
 ):
     """Test retrieval quality for all ground truth queries.
 
@@ -397,7 +274,7 @@ async def test_retrieval_quality_all_queries(
     )
 
 
-async def test_no_results_for_unrelated_query(nc_mcp_client, indexed_manual_pdf):
+async def test_no_results_for_unrelated_query(nc_mcp_client):
     """Test that completely unrelated queries return low/no scores.
 
     The Nextcloud manual shouldn't have relevant content for

tests/unit/client/test_webdav.py

@@ -117,244 +117,3 @@ def test_parse_search_response_with_empty_tags(mocker):
     assert len(results) == 1
     assert "tags" in results[0]
     assert results[0]["tags"] == []
-
-
-@pytest.mark.unit
-async def test_get_file_info_returns_file_details(mocker):
-    """Test that get_file_info returns file info including file ID."""
-    mock_http_client = AsyncMock()
-    client = WebDAVClient(mock_http_client, "testuser")
-
-    # Mock PROPFIND response
-    mock_response = AsyncMock()
-    mock_response.status_code = 207
-    mock_response.content = b"""<?xml version="1.0"?>
-    <d:multistatus xmlns:d="DAV:" xmlns:oc="http://owncloud.org/ns">
-        <d:response>
-            <d:href>/remote.php/dav/files/testuser/Documents/test.pdf</d:href>
-            <d:propstat>
-                <d:prop>
-                    <oc:fileid>12345</oc:fileid>
-                    <d:displayname>test.pdf</d:displayname>
-                    <d:getcontentlength>1024</d:getcontentlength>
-                    <d:getcontenttype>application/pdf</d:getcontenttype>
-                    <d:getlastmodified>Sat, 01 Jan 2025 00:00:00 GMT</d:getlastmodified>
-                    <d:getetag>"abc123"</d:getetag>
-                    <d:resourcetype/>
-                </d:prop>
-            </d:propstat>
-        </d:response>
-    </d:multistatus>"""
-    mock_response.raise_for_status = mocker.Mock()
-
-    mock_http_client.request = AsyncMock(return_value=mock_response)
-
-    # Call get_file_info
-    result = await client.get_file_info("Documents/test.pdf")
-
-    # Verify result
-    assert result is not None
-    assert result["id"] == 12345
-    assert result["name"] == "test.pdf"
-    assert result["path"] == "Documents/test.pdf"
-    assert result["content_type"] == "application/pdf"
-    assert result["size"] == 1024
-    assert result["etag"] == "abc123"
-    assert result["is_directory"] is False
-
-
-@pytest.mark.unit
-async def test_get_file_info_returns_none_for_missing_file(mocker):
-    """Test that get_file_info returns None for missing files."""
-    from httpx import HTTPStatusError, Response
-
-    mock_http_client = AsyncMock()
-    client = WebDAVClient(mock_http_client, "testuser")
-
-    # Mock 404 response
-    mock_response = mocker.Mock(spec=Response)
-    mock_response.status_code = 404
-    mock_http_client.request = AsyncMock(
-        side_effect=HTTPStatusError(
-            "Not Found", request=mocker.Mock(), response=mock_response
-        )
-    )
-
-    # Call get_file_info
-    result = await client.get_file_info("nonexistent.pdf")
-
-    # Verify result is None
-    assert result is None
-
-
-@pytest.mark.unit
-async def test_create_tag_creates_system_tag(mocker):
-    """Test that create_tag creates a system tag via OCS API."""
-    mock_http_client = AsyncMock()
-    client = WebDAVClient(mock_http_client, "testuser")
-
-    # Mock OCS response
-    mock_response = AsyncMock()
-    mock_response.status_code = 200
-    mock_response.json = mocker.Mock(
-        return_value={
-            "ocs": {
-                "data": {
-                    "id": 42,
-                    "name": "vector-index",
-                    "userVisible": True,
-                    "userAssignable": True,
-                }
-            }
-        }
-    )
-    mock_response.raise_for_status = mocker.Mock()
-
-    mock_http_client.post = AsyncMock(return_value=mock_response)
-
-    # Call create_tag
-    result = await client.create_tag("vector-index")
-
-    # Verify result
-    assert result["id"] == 42
-    assert result["name"] == "vector-index"
-    assert result["userVisible"] is True
-    assert result["userAssignable"] is True
-
-    # Verify API call
-    mock_http_client.post.assert_called_once()
-    call_args = mock_http_client.post.call_args
-    assert call_args[0][0] == "/ocs/v2.php/apps/systemtags/api/v1/tags"
-    assert call_args[1]["json"]["name"] == "vector-index"
-
-
-@pytest.mark.unit
-async def test_get_or_create_tag_returns_existing_tag(mocker):
-    """Test that get_or_create_tag returns existing tag without creating."""
-    mock_http_client = AsyncMock()
-    client = WebDAVClient(mock_http_client, "testuser")
-
-    # Mock existing tag
-    mocker.patch.object(
-        client,
-        "get_tag_by_name",
-        return_value={"id": 42, "name": "vector-index", "userVisible": True},
-    )
-    mock_create = mocker.patch.object(client, "create_tag")
-
-    # Call get_or_create_tag
-    result = await client.get_or_create_tag("vector-index")
-
-    # Verify existing tag returned without creating
-    assert result["id"] == 42
-    mock_create.assert_not_called()
-
-
-@pytest.mark.unit
-async def test_get_or_create_tag_creates_new_tag(mocker):
-    """Test that get_or_create_tag creates tag when not found."""
-    mock_http_client = AsyncMock()
-    client = WebDAVClient(mock_http_client, "testuser")
-
-    # Mock no existing tag
-    mocker.patch.object(client, "get_tag_by_name", return_value=None)
-    mocker.patch.object(
-        client,
-        "create_tag",
-        return_value={"id": 42, "name": "vector-index", "userVisible": True},
-    )
-
-    # Call get_or_create_tag
-    result = await client.get_or_create_tag("vector-index")
-
-    # Verify tag was created
-    assert result["id"] == 42
-    client.create_tag.assert_called_once_with("vector-index", True, True)
-
-
-@pytest.mark.unit
-async def test_assign_tag_to_file_success(mocker):
-    """Test that assign_tag_to_file assigns tag via WebDAV."""
-    mock_http_client = AsyncMock()
-    client = WebDAVClient(mock_http_client, "testuser")
-
-    # Mock 201 Created response
-    mock_response = AsyncMock()
-    mock_response.status_code = 201
-
-    mock_http_client.request = AsyncMock(return_value=mock_response)
-
-    # Call assign_tag_to_file
-    result = await client.assign_tag_to_file(12345, 42)
-
-    # Verify result
-    assert result is True
-
-    # Verify API call
-    mock_http_client.request.assert_called_once()
-    call_args = mock_http_client.request.call_args
-    assert call_args[0][0] == "PUT"
-    assert "/systemtags-relations/files/12345/42" in call_args[0][1]
-
-
-@pytest.mark.unit
-async def test_assign_tag_to_file_already_assigned(mocker):
-    """Test that assign_tag_to_file handles already assigned (409) gracefully."""
-    mock_http_client = AsyncMock()
-    client = WebDAVClient(mock_http_client, "testuser")
-
-    # Mock 409 Conflict response (already assigned)
-    mock_response = AsyncMock()
-    mock_response.status_code = 409
-
-    mock_http_client.request = AsyncMock(return_value=mock_response)
-
-    # Call assign_tag_to_file
-    result = await client.assign_tag_to_file(12345, 42)
-
-    # Verify result (should succeed even with 409)
-    assert result is True
-
-
-@pytest.mark.unit
-async def test_remove_tag_from_file_success(mocker):
-    """Test that remove_tag_from_file removes tag via WebDAV."""
-    mock_http_client = AsyncMock()
-    client = WebDAVClient(mock_http_client, "testuser")
-
-    # Mock 204 No Content response
-    mock_response = AsyncMock()
-    mock_response.status_code = 204
-
-    mock_http_client.request = AsyncMock(return_value=mock_response)
-
-    # Call remove_tag_from_file
-    result = await client.remove_tag_from_file(12345, 42)
-
-    # Verify result
-    assert result is True
-
-    # Verify API call
-    mock_http_client.request.assert_called_once()
-    call_args = mock_http_client.request.call_args
-    assert call_args[0][0] == "DELETE"
-    assert "/systemtags-relations/files/12345/42" in call_args[0][1]
-
-
-@pytest.mark.unit
-async def test_remove_tag_from_file_not_assigned(mocker):
-    """Test that remove_tag_from_file handles not assigned (404) gracefully."""
-    mock_http_client = AsyncMock()
-    client = WebDAVClient(mock_http_client, "testuser")
-
-    # Mock 404 Not Found response (tag wasn't assigned)
-    mock_response = AsyncMock()
-    mock_response.status_code = 404
-
-    mock_http_client.request = AsyncMock(return_value=mock_response)
-
-    # Call remove_tag_from_file
-    result = await client.remove_tag_from_file(12345, 42)
-
-    # Verify result (should succeed even with 404)
-    assert result is True

uv.lock

@@ -1936,7 +1936,7 @@ wheels = [
 
 [[package]]
 name = "nextcloud-mcp-server"
-version = "0.48.2"
+version = "0.47.0"
 source = { editable = "." }
 dependencies = [
     { name = "aiosqlite" },