- Replace sequential Qdrant scroll calls with batch retrieve
(50 HTTP requests → 1 request, ~50x faster vector fetch)
- Add point_id to SearchResult to enable batch retrieval by Qdrant point ID
- Reuse query embedding from search algorithm in viz_routes
(eliminates redundant embedding call, saves ~30ms)
- Make BM25 encode() async with thread pool to avoid blocking event loop
(~4.4s was blocking, now properly async)
- Run PCA computation in thread pool to avoid blocking event loop
(~1.2s was blocking, now properly async)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Previously, pymupdf4llm.to_markdown() was called twice - once in
PyMuPDFProcessor during indexing and again in PDFHighlighter during
visualization. Different image path lengths caused different character
offsets, leading to highlighted pages not matching their chunks.
Also fixed issue where all chunks on the same page showed all highlights
instead of just their own highlight. Now restores original page contents
between chunks using xref stream caching.
Changes:
- Add PDFHighlighter class requiring pre-computed page_boundaries and
full_text from document processor (no fallback extraction)
- Pass pre-computed data from processor to highlighter
- Extract page-relative portion of chunk text for cross-page chunks
- Add bounding box highlighting using text anchor search
- Run highlight generation in parallel with embedding/BM25
- Cache and restore page contents to isolate highlights per chunk
Results: Highlighting success rate improved from 51% to 95% (121/128).
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Implements optional context expansion for semantic search results that
fetches adjacent chunks (N-1 and N+1) from Qdrant to provide before/after
context. Removes configurable chunk overlap (default 200 chars) to avoid
duplicate text appearing in both context and excerpt.
Key changes:
- Add include_context and context_chars parameters to nc_semantic_search
and nc_semantic_search_answer tools
- Implement Qdrant cache fast path for chunk retrieval (avoids re-fetching
and re-parsing documents, especially important for PDFs)
- Add _get_chunk_by_index_from_qdrant() to fetch adjacent chunks
- Remove chunk overlap from before_context (last N chars) and after_context
(first N chars) to prevent duplicate text
- Fetch context in parallel with anyio.Semaphore (max 20 concurrent)
- Pass through page_number from SearchResult to SemanticSearchResult
- Remove document-level deduplication (keep chunk-level dedup from algorithm)
Context expansion is opt-in via include_context=true parameter. When enabled:
- Populates has_context_expansion, marked_text, before_context, after_context
- Adds truncation flags when context exceeds context_chars limit
- Falls back to document fetch for legacy data with truncated excerpts
Related: nextcloud_mcp_server/search/context.py:87-382,
nextcloud_mcp_server/server/semantic.py:161-255
Introduces a placeholder-based state tracking system to prevent duplicate
document processing during the gap between scanner queuing and processor
completion.
**Key Changes:**
1. **Placeholder Helper Functions** (`vector/placeholder.py`):
- `write_placeholder_point()` - Creates zero-vector placeholder when queuing
- `query_document_metadata()` - Queries for existing entry (placeholder or real)
- `delete_placeholder_point()` - Removes placeholder before writing real vectors
- `get_placeholder_filter()` - Filters placeholders from user-facing queries
2. **Scanner Updates** (`vector/scanner.py`):
- Replace `indexed_at` comparison with `modified_at` comparison
- Write placeholder before queuing each document
- Query per-document metadata instead of bulk-querying indexed_at
- Fixes bug where files were resubmitted every scan cycle
3. **Processor Updates** (`vector/processor.py`):
- Delete placeholder before upserting real vectors
- Ensures no duplicate points in Qdrant
4. **Query Filters** (all search files):
- Add `get_placeholder_filter()` to all user-facing queries
- Ensures placeholders never appear in search results or visualizations
- Applied to: bm25_hybrid.py, semantic.py, viz_routes.py, algorithms.py
**Architecture:**
- Placeholders use zero vectors with dimension from embedding service
- Payload includes `is_placeholder: True` flag for filtering
- Status field tracks: "pending", "processing", "completed", "failed"
- Deterministic UUIDs using uuid5 for consistent point IDs
**Impact:**
- Eliminates duplicate processing of same documents
- Fixes race condition where long-running documents get queued multiple times
- Prevents scanner from resubmitting files every scan cycle
- Maintains clean separation between in-flight and indexed documents
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
When vector visualization search returns zero results, the code was returning
query_coords: null, which caused JavaScript error "can't access property 0,
queryCoords is null" when the frontend tried to access the array.
Changed to return empty array [] to match expected type and prevent crash.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- algorithms.py: Revert SearchResult.id to int (all docs use int IDs now)
- semantic.py: Revert SemanticSearchResult.id to int, remove Union import
- viz_routes.py: Remove str() conversion when querying doc_id from Qdrant
- viz_routes.py: Convert doc_id from query param to int in chunk context
Fixes vector visualization which was collapsing all chunks to a single
point because Qdrant queries were failing to match doc_id (string vs int).
Major improvements to vector visualization page:
- Refactor PCA to display individual chunks instead of averaged documents
- Add context expansion module for fetching surrounding text from notes and PDFs
- Update deduplication to use (doc_id, doc_type, chunk_start, chunk_end) keys
- Fix Alpine.js rendering with chunk-specific keys including offsets
- Refactor authentication helper to return NextcloudClient for better reuse
- Add async context manager support to NextcloudClient
Technical details:
- viz_routes.py: Fetch specific chunk vectors instead of averaging per document
- context.py: New module supporting both notes and PDF text extraction via PyMuPDF
- search algorithms: Extract page_number, chunk_index, total_chunks from Qdrant
- vector-viz.js/html: Use chunk positions in expansion tracking keys
This enables users to see which specific chunks match their query
and view them with surrounding context in the PCA visualization.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
This commit addresses multiple issues with async operations, PDF metadata
extraction, and type safety in document processing and search.
## Async/Await Fixes
- processor.py:259 - Added await for chunker.chunk_text(content)
- processor.py:270 - Added await for bm25_service.encode_batch(chunk_texts)
- tests/unit/test_document_chunker.py - Converted all 12 test methods to async
## PDF Metadata Enhancement
- pymupdf.py:143 - Added file_size metadata extraction
- pymupdf.py:145-206 - Refactored to extract text page-by-page
- Manually loop through pages instead of using page_chunks=True
- Generate page_boundaries metadata for precise page tracking
- Works around pymupdf.layout.activate() breaking page_chunks=True
- processor.py:32-66 - Added assign_page_numbers() helper function
- Assigns page numbers to chunks based on overlap with page boundaries
- Handles chunks spanning multiple pages
- processor.py:298-300 - Call assign_page_numbers() for PDF files
## Type Safety Fixes
- bm25_hybrid.py:184 - Removed int() conversion of doc_id
- semantic.py:131 - Removed int() conversion of doc_id
- viz_routes.py:275 - Removed int() conversion of doc_id
- Added comments documenting that doc_id can be int (notes) or str (file paths)
## Testing
- All 18 tests passing (12 unit + 6 integration)
- No type errors in modified files
- Container logs show successful processing
- Vector viz searches working correctly
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Get container dimensions before creating Plotly layout to render at correct size immediately
- Add init() method with window resize listener for responsive plot sizing
- Remove post-render resize call (no longer needed with explicit dimensions)
- Improve colorbar positioning and scene domain configuration
This eliminates the visual "jump" during initial render and ensures the plot resizes smoothly when the browser window changes size.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Two fixes for the vector visualization page:
1. **CSS Loading Fix**: Moved CSS <link> from vector_viz.html fragment
to user_info.html <head> block. HTMX fragments don't process <link>
tags in <head>, causing unstyled page. Now CSS loads correctly.
2. **Camera Preservation**: Modified renderPlot() to preserve camera
position when toggling query point visibility. Previously, toggling
the "Show Query Point" checkbox would reset zoom/rotation to default.
Now reads existing camera settings from plot before updating.
Related: nextcloud_mcp_server/auth/static/vector-viz.js:123-130
Related: nextcloud_mcp_server/auth/templates/user_info.html:12
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Extract CSS and JavaScript into separate static files
- Created nextcloud_mcp_server/auth/static/vector-viz.css
- Created nextcloud_mcp_server/auth/static/vector-viz.js
- Updated templates to reference external assets
- Fix vector visualization issues:
- Normalize vectors before PCA to match Qdrant's cosine distance
- Add zero-norm and NaN detection/handling for large datasets
- Enable responsive Plotly sizing (autosize + responsive config)
- Widen plot area to full viewport width with minimized margins
- Improve visualization accuracy:
- Query point now positioned correctly relative to documents
- Handles 200+ points without JSON serialization errors
- Full-width plot maximizes screen space utilization
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
This commit updates the web interface to better align with Nextcloud's
design system and improve the Vector Viz layout.
Changes:
- Replace emoji icons with Material Design SVG icons for better
consistency with Nextcloud apps
- Simplify navigation styling with minimal padding and subtle active
states (250px width)
- Update CSS variables to match Nextcloud design system
- Restructure Vector Viz from two-column to single-column vertical
layout for better plot visibility
- Move search controls to compact horizontal grid at top
- Make navigation toggle always visible (not just on mobile)
- Fix plot container sizing with overflow:visible to prevent colorbar
clipping
- Remove heavy shadows and custom card styling for cleaner aesthetic
- Add error and success page templates with consistent styling
Technical details:
- Preserve Alpine.js for reactive functionality
- Use CSS Grid for responsive horizontal controls layout
- Add smooth transitions for navigation collapse/expand
- Maintain HTMX for dynamic content loading
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
This commit enhances the vector visualization interface with better score
transparency and improved UX:
**Dual-Score Display:**
- Store original algorithm scores before normalization (viz_routes.py:203)
- Display both raw and normalized scores: "Raw Score: 0.842 (89% relative)"
- Update plot hover text with dual scores (userinfo_routes.py:740)
- Fixes issue where all queries showed at least one 100% match regardless
of actual relevance (normalization artifact)
**UI Improvements:**
1. Fusion Method dropdown: Changed from x-show to :disabled
- Prevents jarring layout shift when switching algorithms
- Dropdown stays visible but grayed out when Semantic is selected
- Better UX with opacity: 0.5 and cursor: not-allowed
2. Score Threshold: Changed step from 0.1 to "any"
- Allows arbitrary float precision (0.7, 0.85, 0.123)
- Users can now fine-tune threshold values
3. Document Types: Converted multi-select to checkbox grid
- Replaced clunky Ctrl/Cmd multi-select listbox
- Checkbox grid with cleaner layout
- Positioned left of Score Threshold and Result Limit inputs
- More intuitive UX
**Technical Details:**
- Raw score ranges vary by algorithm:
- Semantic: 0.0-1.0 (cosine similarity)
- BM25 RRF: ~0.001-0.033 (Reciprocal Rank Fusion)
- BM25 DBSF: Can exceed 1.0 (Distribution-Based Score Fusion)
- Normalized scores (0-1) used for visual encoding (marker size, color)
- Original scores preserved in API response via getattr fallback
Files modified:
- nextcloud_mcp_server/auth/viz_routes.py (store original_score)
- nextcloud_mcp_server/auth/templates/vector_viz.html (UI controls)
- nextcloud_mcp_server/auth/userinfo_routes.py (plot hover text)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Fix false-positive validation error where DBSF (Distribution-Based Score
Fusion) correctly produces scores > 1.0 but SearchResult validation
incorrectly rejected them.
**Root Cause**: SearchResult.__post_init__() enforced scores in [0.0, 1.0]
range, but DBSF sums normalized scores from multiple retrieval systems
(dense semantic + sparse BM25), resulting in scores like 1.55 when both
systems strongly agree a document is relevant.
**Changes**:
- Relaxed validation to allow any score ≥ 0.0 (algorithms.py:147-157)
- Updated SearchResult and SemanticSearchResult documentation to explain
score ranges for RRF ([0.0, 1.0]) vs DBSF (unbounded)
- Added comprehensive test coverage for both fusion methods
- Added DBSF fusion option to vector visualization UI
- Updated viz routes and vizApp() to support fusion parameter selection
**Testing**: All 157 unit tests pass, type checking passes, ruff passes
Fixes error: "Configuration error: Score must be between 0.0 and 1.0, got 1.1528953"
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- viz_routes.py: Extract "dense" vector from named vector dict
- semantic.py: Specify using="dense" for BM25 hybrid collections
- Fixes "X must be 2D array" error in hybrid search
- Fixes "Dense vector is not found" error in semantic search
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
The visualization UI was still using the old 'hybrid' algorithm name and
weight parameters that were replaced by the BM25 hybrid search refactor.
This caused "Unknown algorithm: hybrid" errors when using the search
& visualize feature.
Changes:
- Update default algorithm from 'hybrid' to 'bm25_hybrid'
- Update default scoreThreshold from 0.7 to 0.0 to match backend
- Remove deprecated semanticWeight, keywordWeight, fuzzyWeight parameters
- Remove weight parameters from search request
Fixes the visualization search functionality after BM25 hybrid refactor.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Remove obsolete search algorithm imports (Fuzzy, Keyword, Hybrid)
- Update UI to only show Semantic and BM25 Hybrid algorithms
- Replace manual weight controls with RRF fusion info message
- Update default algorithm from "hybrid" to "bm25_hybrid"
- Remove weight parameters (semantic_weight, keyword_weight, fuzzy_weight)
- Update score_threshold default from 0.7 to 0.0 for RRF scoring
- Document ty type checker in CLAUDE.md
Fixes unresolved-import type errors after BM25 refactor.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
This commit addresses critical performance issues with vector visualization
search (reducing time from 40s to ~2s) and improves result visualization
through better visual encoding.
## Performance Fixes
### 1. Fix blocking sleep in retry decorator (base.py:51)
- Changed `time.sleep(5)` to `await anyio.sleep(5)` in @retry_on_429
- Prevents entire event loop from freezing during rate limit retries
- Impact: Reduced search time from 22s to 16s initially
### 2. Add concurrency limiting for verification (verification.py:77-93)
- Added `anyio.Semaphore(20)` to limit concurrent HTTP requests
- Prevents connection pool exhaustion (RequestError) from 90+ simultaneous requests
- Fixes false filtering (was filtering 77/90 results incorrectly)
- Note: Semaphore still in code but verification removed from viz endpoint
### 3. Remove unnecessary verification from viz endpoint (viz_routes.py:483-486)
- Visualization only needs Qdrant metadata (title, excerpt), not full content
- Verification only required for sampling (LLM needs full note content)
- Impact: Reduced search time from 43.7s to ~2s (final fix)
### 4. Restore streaming scanner pattern (scanner.py)
- Process notes one-at-a-time using async generator
- Avoids loading all notes into memory
## Visualization Improvements
### 5. Result-relative score normalization (viz_routes.py:489-504)
- Normalize scores within result set: best=1.0, worst=0.0
- Removes arbitrary RRF normalization (theoretical max didn't make sense)
- Makes visual encoding meaningful regardless of algorithm scores
### 6. Power scaling for marker sizes (userinfo_routes.py:743)
- Changed from linear `8 + (score * 12)` to power `6 + (score² * 14)`
- Creates dramatic visual contrast: 0.0→6px, 0.5→9.5px, 1.0→20px
- Combined with opacity (0.2-1.0) for clear visual hierarchy
### 7. Multi-channel visual encoding (userinfo_routes.py:740-745)
- Size: Exponentially scaled with score²
- Opacity: Linear 0.2-1.0 (keeps all points visible)
- Color: Viridis gradient (blue→yellow)
- Effect: Top results are large/bright/opaque, context results small/dim/transparent
## Result
- Search time: 40s → ~2s (20x faster)
- Visual contrast: Subtle → dramatic (clear result hierarchy)
- No arbitrary cutoffs: All results visible, best naturally highlighted
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Replace asyncio primitives with anyio equivalents throughout the codebase
to establish a single async pattern. This provides better structured
concurrency with automatic cancellation on errors and aligns with the
pytest anyio configuration.
Changes:
- hybrid.py: Replace asyncio.gather() with anyio task groups
- token_broker.py: Replace asyncio.Lock() with anyio.Lock()
- storage.py: Replace asyncio.run() with anyio.run()
- app.py: Replace tg.start_soon() with await tg.start() for task status
- processor.py: Add task_status parameter for structured startup
- scanner.py: Add task_status parameter for structured startup
- CLAUDE.md: Update async/await patterns guidance
The change from start_soon() to await tg.start() enables proper task
initialization signaling, ensuring background tasks are ready before
proceeding. This follows anyio best practices for structured concurrency.
All 118 unit tests pass with the new implementation.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Move access verification from individual search algorithms to final output
stage, eliminating redundant API calls and improving performance.
## Changes
**New:**
- `search/verification.py`: Centralized verification using anyio task groups
- Deduplicates results by (doc_id, doc_type) before verification
- Verifies all unique documents in parallel using structured concurrency
- Filters out inaccessible documents in single pass
**Modified Search Algorithms:**
- `search/semantic.py`: Removed _deduplicate_and_verify() and _verify_document_access()
- `search/keyword.py`: Removed _verify_access() and parallel verification
- `search/fuzzy.py`: Removed _verify_access() and parallel verification
- `search/hybrid.py`: Removed nextcloud_client parameter passing
All algorithms now return unverified results from Qdrant payload.
**Modified Output Stages:**
- `server/semantic.py`: Added verify_search_results() call after search
- `auth/viz_routes.py`: Added verify_search_results() call after search
Both endpoints now verify access once at final stage with deduplication.
## Performance Impact
**Before:**
- Hybrid mode (limit=10): 30 API calls (10 per algorithm × 3 algorithms)
- Single algorithm: 10-20 API calls (with verification buffer)
**After:**
- Hybrid mode (limit=10): 10 API calls (deduplicated verification)
- Single algorithm: 10 API calls (deduplicated verification)
**Performance Gain:** 3x reduction in API calls for hybrid search
## Architecture Benefits
- **Separation of concerns**: Algorithms handle scoring, output stage handles security
- **Deduplication**: Each document verified exactly once
- **Parallel execution**: All verifications run concurrently via anyio task groups
- **Consistency**: Same verification logic across MCP tools and viz endpoints
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Vector Visualization Improvements:
- Add interactive vector viz tab with Alpine.js and Plotly.js to user info page
- Refactor viz route CSS for better scoping and maintainability
- Remove unused nextcloud_host variable
Performance Optimizations:
- Parallelize access verification in fuzzy and keyword search algorithms
- Use asyncio.gather() to verify multiple documents concurrently
- Add exception handling with return_exceptions=True for resilience
Dependencies:
- Update third_party/oidc submodule to include RFC 9728 resource_url support
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Skip tracing for /app/vector-sync/status to reduce noise from HTMX polling.
Metrics collection continues for this endpoint.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Move Webhooks tab to the right (User Info | Vector Sync | Vector Viz | Webhooks)
- Use request.user.display_name instead of session for viz routes
- Fixes session middleware error when accessing via iframe
- Add /app/vector-viz endpoint for interactive search testing
- Implement server-side PCA dimensionality reduction (768-dim → 2D)
- Support multi-select document type filter for cross-app search
- Support all search algorithms: semantic, keyword, fuzzy, hybrid
- Display 2D scatter plot of vector embeddings using Plotly
- Show search results with scores and document types
- Register viz routes in app.py
- Add custom PCA implementation using numpy eigendecomposition
- Replace sklearn.decomposition.PCA with custom implementation
- Maintains same API (fit, transform, fit_transform)
- Supports explained_variance_ratio_ for variance analysis
- Removes scikit-learn dependency from project
- Add type hints and assertion for type safety
Fixes layout issues on the webhooks admin tab:
- Add min-height to container to fill viewport consistently
- Use CSS Grid to overlay tab panes without jumpiness
- Add smooth htmx fade transitions for content swaps
- Adjust vector sync polling interval from 3s to 10s
- Add .playwright-mcp/ to gitignore for test screenshots
The CSS Grid approach allows tabs to overlay without absolute positioning,
preventing content cutoff while maintaining smooth transitions without
container resizing jumps.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Implement real-time vector sync status updates in the /app UI without
requiring page refreshes. The status (indexed documents, pending
documents, sync state) now updates automatically every 3 seconds.
Changes:
- Add vector_sync_status_fragment() endpoint that returns HTML fragment
with current vector sync status
- Modify user_info_html() to use htmx loading for vector sync section
with hx-trigger="load" on initial render
- Status fragment includes hx-trigger="every 3s" for continuous polling
- Add /app/vector-sync/status route to browser_routes
The implementation uses htmx (already loaded on page) to poll the status
endpoint, providing near real-time updates with minimal overhead. The
endpoint queries Qdrant for indexed count and reads from memory streams
for pending count, returning only the status HTML fragment.
Pattern follows existing webhook management UI which also uses htmx
for dynamic loading.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Simplified the webapp routing structure by consolidating the admin UI
to a single clean endpoint.
Changes:
- Moved webapp from /user/page to /app (root of mount)
- Removed /user JSON endpoint (no longer needed)
- Updated mount point from /user to /app in app.py
- Updated all route path checks (3 locations)
- Updated OAuth redirects to point to /app
- Updated all HTMX endpoint references
- Updated documentation (ADR-007, CHANGELOG)
- Added redirect from /app to /app/ for trailing slash handling
New Route Structure:
- /app - Main webapp (HTML UI with tabs)
- /app/revoke - Revoke background access
- /app/webhooks - Webhook management UI
- /app/webhooks/enable/{preset_id} - Enable webhook preset
- /app/webhooks/disable/{preset_id} - Disable webhook preset
Breaking Change: Existing bookmarks to /user or /user/page will no longer work.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Refactored the storage system to use a unified SQLite database for both
webhook tracking and OAuth token storage, available in both BasicAuth
and OAuth modes.
Changes:
- Renamed refresh_token_storage.py → storage.py
- Made TOKEN_ENCRYPTION_KEY optional (only required for OAuth token ops)
- Added registered_webhooks table with schema versioning
- Added webhook storage methods (store, get, delete, list, clear)
- Initialize storage in both BasicAuth and OAuth modes
- Updated webhook routes to persist registrations in database
- Database-first pattern for webhook status checks (performance)
- Updated all imports across codebase
Storage Behavior:
- Database created automatically at startup if needed
- Existing databases detected and reused
- Server fails fast if database initialization fails
- No migrations needed (OAuth feature is experimental)
Testing:
- Added 13 comprehensive unit tests for webhook storage
- All 118 unit tests pass
- All 5 smoke tests pass
- Verified fail-fast behavior on initialization errors
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Simplifies the OpenTelemetry tracing setup by removing the redundant
OTEL_ENABLED flag and using the presence of OTEL_EXPORTER_OTLP_ENDPOINT
to determine if tracing should be enabled. This follows the standard
OpenTelemetry environment variable conventions more closely.
Changes:
- Remove OTEL_ENABLED/tracing_enabled flag in favor of checking if
OTEL_EXPORTER_OTLP_ENDPOINT is set
- Add OTEL_EXPORTER_VERIFY_SSL configuration option for OTLP endpoints
with self-signed certificates (defaults to false for development)
- Move HTTPXClientInstrumentor initialization to module level to ensure
httpx calls are traced across all Nextcloud API requests
- Add tracing spans to vector sync operations (scan_user_documents)
- Fix authorization header logging to only warn about missing headers
in OAuth mode (BasicAuth mode doesn't use Authorization headers)
- Update observability documentation to reflect simplified configuration
- Refactor Dockerfile to use --no-editable flag for uv sync
Breaking changes:
- OTEL_ENABLED environment variable is removed
- Tracing is now automatically enabled when OTEL_EXPORTER_OTLP_ENDPOINT
is set
Migration guide:
- Remove OTEL_ENABLED=true from environment configuration
- Tracing will be enabled automatically if OTEL_EXPORTER_OTLP_ENDPOINT
is configured
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
This PR enables safe switching between embedding models and multi-server
deployments by implementing auto-generated Qdrant collection names based on
deployment ID and model name.
## Problem
Previously, all deployments used a single hardcoded collection name
"nextcloud_content", which caused two critical issues:
1. **Dimension mismatches when switching models**: Changing
OLLAMA_EMBEDDING_MODEL (e.g., nomic-embed-text at 768D → all-minilm at
384D) would cause runtime errors as vectors couldn't be inserted into a
collection with incompatible dimensions.
2. **Collection collisions in multi-server setups**: Multiple MCP servers
sharing a single Qdrant instance would overwrite each other's data,
making horizontal scaling impossible.
## Solution
### Auto-Generated Collection Naming
Collections are now automatically named using the pattern:
\`{deployment-id}-{model-name}\`
**Deployment ID**: Uses \`OTEL_SERVICE_NAME\` if configured (and not default
value), otherwise falls back to \`hostname\` for simple Docker deployments.
**Model Name**: From \`OLLAMA_EMBEDDING_MODEL\` with path separators sanitized.
**Examples**:
- \`my-mcp-server-nomic-embed-text\` (with OTEL_SERVICE_NAME=my-mcp-server)
- \`mcp-container-all-minilm\` (simple Docker, hostname=mcp-container)
**Override**: Users can still set \`QDRANT_COLLECTION\` explicitly to bypass
auto-generation for backward compatibility.
### Dimension Validation
Added startup validation that checks collection dimensions match the
embedding service. If a mismatch is detected, the server fails fast with a
clear error message explaining:
- Expected vs actual dimensions
- Likely cause (model change)
- Solutions (delete collection, use different name, or revert model)
### Improved Sampling Error Handling
Enhanced MCP sampling rejection handling to treat user rejections as normal
behavior rather than errors:
- **User rejections** ("rejected", "denied") → INFO log, no traceback
- **Unsupported clients** → INFO log, no traceback
- **Other MCP errors** → WARNING log, no traceback
- **Unexpected errors** → ERROR log WITH traceback
This aligns with the MCP specification where clients SHOULD prompt users for
approval/denial of sampling requests.
## Changes
### Core Implementation
- **nextcloud_mcp_server/config.py**: Added \`get_collection_name()\` method
with deployment ID detection and model name sanitization
- **nextcloud_mcp_server/vector/qdrant_client.py**: Dimension validation on
collection open with helpful error messages
- **nextcloud_mcp_server/vector/{scanner,processor}.py**: Updated to use
\`get_collection_name()\`
- **nextcloud_mcp_server/auth/userinfo_routes.py**: Vector sync status uses
\`get_collection_name()\`
- **nextcloud_mcp_server/server/semantic.py**:
- Updated semantic search tools to use \`get_collection_name()\`
- Improved sampling rejection error handling (McpError vs Exception)
### Documentation
- **docs/semantic-search-architecture.md**: New comprehensive architecture
document (557 lines) covering background sync, semantic search flow, RAG
implementation, and deployment modes
- **docs/configuration.md**: Added detailed "Qdrant Collection Naming"
section with examples and multi-server deployment guidance
- **docker-compose.yml**: Added comments explaining collection naming behavior
- **README.md**: Updated semantic search descriptions to clarify
experimental status, Notes-only support, and infrastructure requirements
## Migration Guide
**For existing single-server deployments:**
Option 1 (Recommended): Use explicit collection name for continuity
\`\`\`bash
QDRANT_COLLECTION=nextcloud_content # Keep existing collection
\`\`\`
Option 2: Allow auto-generation and re-embed
\`\`\`bash
# Remove QDRANT_COLLECTION override
# New collection will be created based on deployment ID + model
# Requires re-embedding all documents (may take time)
\`\`\`
**For new multi-server deployments:**
Set unique OTEL service names per server:
\`\`\`bash
# Server 1
OTEL_SERVICE_NAME=mcp-prod
OLLAMA_EMBEDDING_MODEL=nomic-embed-text
# → Collection: "mcp-prod-nomic-embed-text"
# Server 2
OTEL_SERVICE_NAME=mcp-staging
OLLAMA_EMBEDDING_MODEL=nomic-embed-text
# → Collection: "mcp-staging-nomic-embed-text"
\`\`\`
## Benefits
✅ **Safe model switching**: Each model gets its own collection, preventing
dimension mismatch errors
✅ **Multi-server support**: Multiple MCP servers can share one Qdrant
instance without conflicts
✅ **Clear ownership**: Collection names show which deployment and model owns
the data
✅ **Better error messages**: Dimension validation provides actionable
guidance
✅ **Backward compatible**: Existing deployments can continue using
\`QDRANT_COLLECTION\` override
## Testing
Validated with:
- Single-server deployments (default hostname-based naming)
- Multi-server deployments (OTEL service name-based naming)
- Model switching scenarios (dimension validation)
- Collection override scenarios (backward compatibility)
Next steps: Testing various Ollama embedding models to investigate optimal
chunk sizes and performance characteristics.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Add real-time processing status display to the browser UI at /user/page
showing indexed document count, pending queue size, and sync status.
Implements the status display described in ADR-007 lines 280-298.
Changes:
- Store document_queue and related state in app.state for route access
- Add _get_processing_status() helper to query Qdrant and check queue
- Display status section in user_info_html() with indexed/pending counts
- Show color-coded status badge (green "Idle" or orange "Syncing")
- Only displays when VECTOR_SYNC_ENABLED=true
Status appears in both BasicAuth and OAuth modes, positioned after
session info but before logout buttons. Numbers are formatted with
commas for readability.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Implements background vector database synchronization using anyio
TaskGroups for BasicAuth mode with single-user credentials.
Scanner Implementation:
- Periodic document discovery (hourly, configurable)
- Timestamp-based change detection (Nextcloud vs Qdrant)
- Wake event for immediate scanning on-demand
- Supports both initial sync (all docs) and incremental sync (changes only)
- Detects deleted documents and queues for removal
Processor Implementation:
- Concurrent document processing pool (3 workers default)
- I/O-bound embedding generation via Ollama API
- Retry logic with exponential backoff (3 retries)
- Document chunking (512 words, 50-word overlap)
- Handles both index and delete operations
- Upserts vectors to Qdrant with rich metadata
App Lifespan Integration:
- Extended AppContext with background task state
- Modified app_lifespan_basic() to start tasks via anyio TaskGroups
- Graceful shutdown with coordinated task cancellation
- Only activates when VECTOR_SYNC_ENABLED=true
Embedding Service:
- OllamaEmbeddingProvider with TLS support
- Singleton pattern for shared client instances
- Batch embedding support for efficiency
- Auto-detects embedding dimension (768 for nomic-embed-text)
Qdrant Client:
- Async client wrapper with singleton pattern
- Auto-creates collection on first use
- COSINE distance metric for semantic similarity
- Integrates with embedding service for dimension detection
Health Check Enhancement:
- Added Qdrant status check to /health/ready endpoint
- Only checks when VECTOR_SYNC_ENABLED=true
- 2-second timeout for health probe
- Reports connection errors with details
Configuration:
- VECTOR_SYNC_ENABLED: Enable background sync
- VECTOR_SYNC_SCAN_INTERVAL: Scanner frequency (3600s default)
- VECTOR_SYNC_PROCESSOR_WORKERS: Concurrent processors (3 default)
- QDRANT_URL, QDRANT_API_KEY, QDRANT_COLLECTION: Vector DB config
- OLLAMA_BASE_URL, OLLAMA_EMBEDDING_MODEL: Embedding service config
Dependencies Added:
- qdrant-client>=1.7.0: Vector database client
Docker Compose:
- Added Qdrant service with health check
- Exposed ports 6333 (REST) and 6334 (gRPC)
- Configured MCP service with vector sync environment
- Added qdrant-data volume for persistence
Known Issue:
- FastMCP lifespan not triggering for streamable-http transport
- Background tasks will start once lifespan integration is complete
- Lifespan triggers on MCP session establishment, not server startup
Related: ADR-007 Background Vector Database Synchronization
🤖 Generated with Claude Code (https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
This commit adds proper integration testing of the login elicitation flow
(ADR-006) using python-sdk's MCP client with actual elicitation callback
support, and fixes user_id extraction to support both JWT and opaque tokens.
## Changes
### 1. Enhanced create_mcp_client_session helper (tests/conftest.py)
- Added `elicitation_callback` parameter to function signature
- Pass callback to ClientSession constructor
- Added necessary imports: RequestContext, ElicitRequestParams,
ElicitResult, ErrorData from mcp package
- Allows fixtures to provide custom elicitation handlers
### 2. New fixture: nc_mcp_oauth_client_with_elicitation (tests/conftest.py)
- Creates MCP client with Playwright-based elicitation callback
- Callback implementation:
- Extracts OAuth URL from elicitation message using regex
- Uses Playwright browser to complete OAuth flow automatically
- Handles Nextcloud login form (username/password)
- Handles consent screen if present
- Waits for OAuth callback completion
- Returns ElicitResult(action="accept") on success
- Function-scoped to allow independent test state
- Tracks elicitation invocations via session.elicitation_triggered
### 3. Fixed user_id extraction for opaque tokens (oauth_tools.py)
- Created extract_user_id_from_token() helper to handle both JWT and
opaque tokens by calling userinfo endpoint when needed
- Fixed check_logged_in to use helper instead of broken ctx.authorization
- Fixed revoke_nextcloud_access to use helper instead of ctx.context.get()
- Both tools now properly extract user_id from access tokens
### 4. Enhanced integration tests (test_elicitation_integration.py)
- Updated tests to revoke refresh tokens via MCP tool
- All 4 tests now pass:
- test_check_logged_in_with_real_elicitation_callback: Complete flow
- test_elicitation_callback_url_extraction: URL extraction validation
- test_elicitation_stores_refresh_token: Token persistence verification
- test_second_check_logged_in_does_not_elicit: No redundant elicitations
### 5. Added diagnostic logging (oauth_routes.py)
- Track user_id extraction from ID tokens during OAuth callbacks
- Log refresh token storage with user_id and flow_type
## Test Results
✅ 4/4 tests pass
The test suite successfully validates:
- Elicitation callback is triggered when no refresh token exists
- Playwright automation completes OAuth flow
- Refresh token is stored after OAuth with correct user_id
- Tool returns "yes" after successful login
- Already-logged-in users don't get redundant elicitations
## Why This Matters
Previous tests (test_login_elicitation.py) only validated response
formats and acknowledged they couldn't test real elicitation protocol.
This test exercises the REAL MCP elicitation protocol end-to-end:
1. MCP server calls ctx.elicit()
2. python-sdk ClientSession invokes custom callback
3. Callback completes OAuth via Playwright
4. Client returns acceptance to server
5. Tool proceeds with authenticated state
This proves the python-sdk MCP client can handle elicitation in
production environments with both JWT and opaque tokens.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
This commit addresses the "Login not detected" issue after completing
OAuth login via elicitation by unifying the session architecture and
adding comprehensive visibility into background session status.
## Changes
### 1. Enhanced check_logged_in with comprehensive logging (oauth_tools.py)
- Added detailed logging at each step of token lookup
- Implemented fallback strategy: first search by provisioning_client_id,
then fall back to user_id lookup
- This allows detection of refresh tokens created via any flow
(elicitation or browser login)
- Log messages include flow_type, provisioned_at, and provisioning_client_id
for debugging
### 2. Unified session architecture (browser_oauth_routes.py)
- Browser login now stores provisioning_client_id=state when saving
refresh token
- This makes browser and elicitation flows consistent - both can be
found by the same state parameter
- Treats Flow 2 (elicitation) and browser login as the same "background
session"
### 3. Enhanced /user/page with session status (userinfo_routes.py)
- Added comprehensive background access section showing:
- Background Access: Granted/Not Granted (with visual indicators)
- Flow Type: browser/flow2/hybrid
- Provisioned At: timestamp
- Token Audience: nextcloud/mcp
- Scopes: detailed scope list
- Status displayed regardless of which flow created the session
(browser login or elicitation)
### 4. Added revoke functionality (userinfo_routes.py, app.py)
- New POST endpoint: /user/revoke
- Allows users to revoke background access (delete refresh token)
- Browser session cookie remains valid for UI access
- Confirmation dialog before revocation
- Success page with auto-redirect back to /user/page
- Registered route in app.py browser_routes
## Testing
All tests pass:
- 6/6 login elicitation tests pass
- 21/21 core OAuth tests pass
- Comprehensive logging helps debug future issues
## Fixes
Resolves: "Login not detected. Please ensure you completed the login
at the provided URL before clicking OK."
The issue occurred because elicitation and browser login created
separate sessions. Now they are unified under the same architecture.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
This PR fixes multiple OAuth-related issues:
## Unified OAuth Callback
- Consolidated `/oauth/callback-nextcloud` and `/oauth/login-callback` into single `/oauth/callback` endpoint
- Flow type determined by session lookup via state parameter (no query params in redirect_uri)
- Fixes redirect_uri validation issues with IdPs requiring exact match
- Legacy endpoints kept as aliases for backwards compatibility
## PKCE Implementation
- Implemented PKCE (RFC 7636) for Flow 2 (resource provisioning)
- Generate code_verifier and code_challenge
- Store code_verifier in session storage
- Retrieve and use in token exchange
- Fixed PKCE for browser login (integrated mode)
- Previously only worked for external IdP (Keycloak)
- Now works for both Nextcloud OIDC and external IdP
## Login Elicitation Fixes (ADR-006)
- Fixed elicitation URL to route through MCP server endpoint
- Changed from direct Nextcloud URL to `/oauth/authorize-nextcloud`
- Ensures PKCE is properly handled by server
- Fixed login detection after OAuth flow completes
- Look up refresh token by state parameter instead of user_id
- Works even when Flow 1 token not present
- Added `get_refresh_token_by_provisioning_client_id()` method
## Session Authentication
- Fixed `/user/page` redirect loop
- Shared oauth_context with mounted browser_app
- SessionAuthBackend can now validate sessions correctly
## Tests
- Added comprehensive login elicitation test suite
- Updated scope authorization test expectations
- All 43 OAuth tests passing
## Files Changed
- `app.py`: Shared oauth_context, unified callback route
- `oauth_routes.py`: Unified callback, PKCE for Flow 2
- `browser_oauth_routes.py`: PKCE for integrated mode
- `oauth_tools.py`: Fixed elicitation URL generation
- `refresh_token_storage.py`: Added lookup by provisioning_client_id
- `test_login_elicitation.py`: New test suite
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
This commit completes the OAuth audience validation implementation per RFC 7519,
RFC 8707 (Resource Indicators), and RFC 9728 (Protected Resource Metadata).
## Key Changes
### OAuth Resource Parameters (RFC 8707)
- Add `resource` parameter to Flow 1 (MCP client auth) with MCP server audience
- Add `resource` parameter to Flow 2 (Nextcloud access) with Nextcloud audience
- Add `nextcloud_resource_uri` to oauth_context configuration
- Fix undefined variable error in starlette_lifespan
### PRM-Based Resource Discovery (RFC 9728)
- Update tests to fetch resource identifier from PRM endpoint
- Add fallback to hardcoded value if PRM fetch fails
- Demonstrate correct OAuth client implementation pattern
### ADR-005 Documentation Updates
- Update to reflect simplified RFC 7519 compliant implementation
- Document that MCP validates only its own audience (not Nextcloud's)
- Add section on OAuth resource parameters and PRM discovery
- Update implementation checklist to show completed items
- Mark status as "Implemented" with update date
## Implementation Details
The solution follows RFC 7519 Section 4.1.3: resource servers validate only
their own presence in the audience claim. This simplifies the logic while
maintaining security:
- MCP server validates MCP audience only
- Nextcloud independently validates its own audience
- No dual validation required at MCP layer
- Token reuse is allowed per RFC 8707 for multi-audience tokens
## Test Results
✅ test_mcp_oauth_server_connection - PASSED
✅ test_deck_board_view_permissions - PASSED
✅ test_prm_endpoint - PASSED
All OAuth flows now properly specify target resources, resulting in correct
audience validation throughout the system.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Since both multi-audience and exchange modes now validate the same thing
(MCP audience only per RFC 7519), consolidated the duplicate methods:
- Removed duplicate verification methods (_verify_multi_audience_token
and _verify_mcp_audience_only)
- Created single _verify_mcp_audience() method for all validation
- Removed duplicate helper (_validate_multi_audience), kept _has_mcp_audience
- Mode only affects logging and what happens AFTER verification
The mode distinction is now purely about post-verification behavior:
- Multi-audience mode: Use token directly (Nextcloud validates its own)
- Exchange mode: Exchange for Nextcloud-audience token via RFC 8693
This makes the code cleaner and clearer about what's actually happening -
both modes do identical validation, they just differ in how the validated
token is used.
All tests pass: unit (65), OAuth integration confirmed working.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Per RFC 7519 Section 4.1.3, resource servers should only validate their
own presence in the audience claim, not check for other resource servers.
Changes:
- UnifiedTokenVerifier now validates only MCP audience (not Nextcloud's)
- Nextcloud independently validates its own audience when receiving API calls
- This is NOT token passthrough (we validate tokens before use)
- This IS token reuse which is explicitly allowed by RFC 8707
Updates:
- Simplified _validate_multi_audience() to follow OAuth spec
- Updated docstrings and comments to clarify RFC 7519 compliance
- Fixed unit tests that expected dual-audience validation
- Updated ADR-005 to document the correct OAuth interpretation
- All tests pass: unit (65), smoke (5), OAuth integration
This makes the implementation simpler, more maintainable, and properly
aligned with OAuth 2.0 specifications while maintaining security.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Fix two issues preventing OAuth tests from passing:
1. Set oidc_client_id and oidc_client_secret on Settings object
- These were being read from environment but not propagated to the
UnifiedTokenVerifier settings instance
2. Use client_issuer instead of issuer for JWT validation
- client_issuer accounts for NEXTCLOUD_PUBLIC_ISSUER_URL override
- Fixes "Invalid issuer" errors when public URL differs from internal
3. Accept resource URL with /mcp path in audience validation
- During DCR, resource_url is registered as "{mcp_server_url}/mcp"
- Tokens correctly include this full path as audience
- Verifier now accepts both "http://localhost:8001" and
"http://localhost:8001/mcp" as valid MCP audiences
These changes restore OAuth functionality while maintaining ADR-005
security requirements for proper audience validation.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Replace two non-compliant token verifiers (NextcloudTokenVerifier and
ProgressiveConsentTokenVerifier) with a single UnifiedTokenVerifier that properly
validates token audiences per MCP Security Best Practices specification.
The previous implementation had a critical security vulnerability where tokens
intended for the MCP server were passed directly to Nextcloud APIs without
proper audience validation (token passthrough anti-pattern). This violates
OAuth 2.0 security principles and the MCP specification.
Changes:
- Add UnifiedTokenVerifier supporting two compliant modes:
* Multi-audience mode (default): Validates tokens contain BOTH MCP and
Nextcloud audiences, enabling direct use without exchange
* Token exchange mode (opt-in): Validates MCP audience only, exchanges
for Nextcloud tokens via RFC 8693 with caching to minimize latency
- Remove token passthrough vulnerability from context.py and context_helper.py
- Implement token exchange caching (5-minute TTL default) to reduce network calls
- Add required environment variables for audience validation:
* NEXTCLOUD_MCP_SERVER_URL - MCP server URL (used as audience)
* NEXTCLOUD_RESOURCE_URI - Nextcloud resource identifier
* TOKEN_EXCHANGE_CACHE_TTL - Cache TTL for exchanged tokens
- Update docker-compose.yml with resource URI configuration for both OAuth modes
- Add comprehensive test suite (29 tests) covering both authentication modes
- Remove legacy NextcloudTokenVerifier and ProgressiveConsentTokenVerifier
Security improvements:
- Eliminates token passthrough anti-pattern
- Enforces proper audience separation between MCP and Nextcloud
- Complies with MCP Security Best Practices and RFC 8707/8693
- Maintains performance with token exchange caching
Test results: 65/65 unit tests passed, 5/5 smoke tests passed
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
The previous commit made audience validation too strict by requiring the
MCP client ID in the audience claim. This broke Nextcloud's user_oidc JWT
tokens which use the redirect URI (resource URL) as the audience instead
of the client ID.
Changes:
- Accept tokens with MCP client ID in audience (Keycloak multi-audience)
- Accept tokens with resource URL in audience (Nextcloud JWT redirect URI)
- Accept tokens with no audience (backward compatibility)
- Reject only tokens with "nextcloud" audience (wrong flow - Flow 2 tokens)
This preserves the security boundary between Flow 1 (MCP session tokens)
and Flow 2 (Nextcloud access tokens) while supporting both Keycloak's
multi-audience tokens and Nextcloud's resource URL audience pattern.
All OAuth tests pass, including:
- test_mcp_oauth_server_connection (JWT with resource URL audience)
- test_jwt_tool_list_operations (JWT token validation)
- test_jwt_multiple_operations (token persistence)
- test_token_exchange_basic (Keycloak multi-audience tokens)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Configure Keycloak authorization policies to allow nextcloud-mcp-server
to exchange tokens for nextcloud audience. This enables RFC 8693 token
exchange flow between the MCP client and Nextcloud.
Changes:
- Enable service accounts and authorization services for nextcloud client
- Add token-exchange resource with scope-based permissions
- Create client policy allowing nextcloud-mcp-server and nextcloud
- Add token-exchange-permission with affirmative decision strategy
- Add mcp-server-audience mapper to nextcloud-mcp-server client
- Simplify audience validation to accept tokens with MCP client ID
The authorization policy enables tokens issued to nextcloud-mcp-server
to be exchanged for tokens with nextcloud audience, validated via both
clients being included in the allow-nextcloud-mcp-server-to-exchange
policy.
All 7 token exchange integration tests pass, confirming:
- Basic token exchange with correct audience claims
- Nextcloud API access with exchanged tokens
- Stateless multiple exchange operations
- Full CRUD operations on Notes API
- Proper claim preservation (sub, azp, aud)
- Default scope configuration
- TokenExchangeService implementation
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Chris Coutinho