tools-only Task #19 Complete: MCP Server Integration for Vector Databases
Completion Date: February 7, 2026 Status: ✅ Complete Tests: 8/8 passing
Objective
Extend the MCP server to expose the 4 new vector database adaptors (Weaviate, Chroma, FAISS, Qdrant) as MCP tools, enabling Claude AI assistants to export skills directly to vector databases.
Implementation Summary
Files Created
src/skill_seekers/mcp/tools/vector_db_tools.py (500+ lines)
- 4 async implementation functions
- Comprehensive docstrings with examples
- Error handling for missing directories/adaptors
- Usage instructions with code examples
- Links to official documentation
tests/test_mcp_vector_dbs.py (274 lines)
- 8 comprehensive test cases
- Test fixtures for skill directories
- Validation of exports, error handling, and output format
- All tests passing (8/8)
Files Modified
src/skill_seekers/mcp/tools/init.py
- Added vector_db_tools module to docstring
- Imported 4 new tool implementations
- Added to all exports
src/skill_seekers/mcp/server_fastmcp.py
- Updated docstring from "21 tools" to "25 tools"
- Added 6th category: "Vector Database tools"
- Imported 4 new implementations (both try/except blocks)
- Registered 4 new tools with @safe_tool_decorator
- Added VECTOR DATABASE TOOLS section (125 lines)
New MCP Tools
1. export_to_weaviate
Description: Export skill to Weaviate vector database format (hybrid search, 450K+ users)
Parameters:
skill_dir(str): Path to skill directoryoutput_dir(str, optional): Output directory
Output: JSON file with Weaviate schema, objects, and configuration
Usage Instructions Include:
- Python code for uploading to Weaviate
- Hybrid search query examples
- Links to Weaviate documentation
2. export_to_chroma
Description: Export skill to Chroma vector database format (local-first, 800K+ developers)
Parameters:
skill_dir(str): Path to skill directoryoutput_dir(str, optional): Output directory
Output: JSON file with Chroma collection data
Usage Instructions Include:
- Python code for loading into Chroma
- Query collection examples
- Links to Chroma documentation
3. export_to_faiss
Description: Export skill to FAISS vector index format (billion-scale, GPU-accelerated)
Parameters:
skill_dir(str): Path to skill directoryoutput_dir(str, optional): Output directory
Output: JSON file with FAISS embeddings, metadata, and index config
Usage Instructions Include:
- Python code for building FAISS index (Flat, IVF, HNSW options)
- Search examples
- Index saving/loading
- Links to FAISS documentation
4. export_to_qdrant
Description: Export skill to Qdrant vector database format (native filtering, 100K+ users)
Parameters:
skill_dir(str): Path to skill directoryoutput_dir(str, optional): Output directory
Output: JSON file with Qdrant collection data and points
Usage Instructions Include:
- Python code for uploading to Qdrant
- Search with filters examples
- Links to Qdrant documentation
Test Coverage
Test Cases (8/8 passing)
- test_export_to_weaviate - Validates Weaviate export with output verification
- test_export_to_chroma - Validates Chroma export with output verification
- test_export_to_faiss - Validates FAISS export with output verification
- test_export_to_qdrant - Validates Qdrant export with output verification
- test_export_with_default_output_dir - Tests default output directory behavior
- test_export_missing_skill_dir - Validates error handling for missing directories
- test_all_exports_create_files - Validates file creation for all 4 exports
- test_export_output_includes_instructions - Validates usage instructions in output
Test Results
tests/test_mcp_vector_dbs.py::test_export_to_weaviate PASSED
tests/test_mcp_vector_dbs.py::test_export_to_chroma PASSED
tests/test_mcp_vector_dbs.py::test_export_to_faiss PASSED
tests/test_mcp_vector_dbs.py::test_export_to_qdrant PASSED
tests/test_mcp_vector_dbs.py::test_export_with_default_output_dir PASSED
tests/test_mcp_vector_dbs.py::test_export_missing_skill_dir PASSED
tests/test_mcp_vector_dbs.py::test_all_exports_create_files PASSED
tests/test_mcp_vector_dbs.py::test_export_output_includes_instructions PASSED
8 passed in 0.35s
Integration Architecture
MCP Server Structure
MCP Server (25 tools, 6 categories)
├── Config tools (3)
├── Scraping tools (8)
├── Packaging tools (4)
├── Splitting tools (2)
├── Source tools (4)
└── Vector Database tools (4) ← NEW
├── export_to_weaviate
├── export_to_chroma
├── export_to_faiss
└── export_to_qdrant
Tool Implementation Pattern
Each tool follows the FastMCP pattern:
@safe_tool_decorator(description="...")
async def export_to_<target>(
skill_dir: str,
output_dir: str | None = None,
) -> str:
"""Tool docstring with args and returns."""
args = {"skill_dir": skill_dir}
if output_dir:
args["output_dir"] = output_dir
result = await export_to_<target>_impl(args)
if isinstance(result, list) and result:
return result[0].text if hasattr(result[0], "text") else str(result[0])
return str(result)
Usage Examples
Claude Desktop MCP Config
{
"mcpServers": {
"skill-seeker": {
"command": "python",
"args": ["-m", "skill_seekers.mcp.server_fastmcp"]
}
}
}
Using Vector Database Tools
Example 1: Export to Weaviate
export_to_weaviate(
skill_dir="output/react",
output_dir="output"
)
Example 2: Export to Chroma with default output
export_to_chroma(skill_dir="output/django")
Example 3: Export to FAISS
export_to_faiss(
skill_dir="output/fastapi",
output_dir="/tmp/exports"
)
Example 4: Export to Qdrant
export_to_qdrant(skill_dir="output/vue")
Output Format Example
Each tool returns comprehensive instructions:
✅ Weaviate Export Complete!
📦 Package: react-weaviate.json
📁 Location: output/
📊 Size: 45,678 bytes
🔧 Next Steps:
1. Upload to Weaviate:
```python
import weaviate
import json
client = weaviate.Client("http://localhost:8080")
data = json.load(open("output/react-weaviate.json"))
# Create schema
client.schema.create_class(data["schema"])
# Batch upload objects
with client.batch as batch:
for obj in data["objects"]:
batch.add_data_object(obj["properties"], data["class_name"])
- Query with hybrid search:
result = client.query.get(data["class_name"], ["content", "source"]) \ .with_hybrid("React hooks usage") \ .with_limit(5) \ .do()
📚 Resources:
- Weaviate Docs: https://weaviate.io/developers/weaviate
- Hybrid Search: https://weaviate.io/developers/weaviate/search/hybrid
---
## Technical Achievements
### 1. Consistent Interface
All 4 tools share the same interface:
- Same parameter structure
- Same error handling pattern
- Same output format (TextContent with detailed instructions)
- Same integration with existing adaptors
### 2. Comprehensive Documentation
Each tool includes:
- Clear docstrings with parameter descriptions
- Usage examples in output
- Python code snippets for uploading
- Query examples for searching
- Links to official documentation
### 3. Robust Error Handling
- Missing skill directory detection
- Adaptor import failure handling
- Graceful fallback for missing dependencies
- Clear error messages with suggestions
### 4. Complete Test Coverage
- 8 test cases covering all scenarios
- Fixture-based test setup for reusability
- Validation of structure, content, and files
- Error case testing
---
## Impact
### MCP Server Expansion
- **Before:** 21 tools across 5 categories
- **After:** 25 tools across 6 categories (+19% growth)
- **New Capability:** Direct vector database export from MCP
### Vector Database Support
- **Weaviate:** Hybrid search (vector + BM25), 450K+ users
- **Chroma:** Local-first development, 800K+ developers
- **FAISS:** Billion-scale search, GPU-accelerated
- **Qdrant:** Native filtering, 100K+ users
### Developer Experience
- Claude AI assistants can now export skills to vector databases directly
- No manual CLI commands needed
- Comprehensive usage instructions included
- Complete end-to-end workflow from scraping to vector database
---
## Integration with Week 2 Adaptors
Task #19 completes the MCP integration of Week 2's vector database adaptors:
| Task | Feature | MCP Integration |
|------|---------|-----------------|
| #10 | Weaviate Adaptor | ✅ export_to_weaviate |
| #11 | Chroma Adaptor | ✅ export_to_chroma |
| #12 | FAISS Adaptor | ✅ export_to_faiss |
| #13 | Qdrant Adaptor | ✅ export_to_qdrant |
---
## Next Steps (Week 3)
With Task #19 complete, Week 3 can begin:
- **Task #20:** GitHub Actions automation
- **Task #21:** Docker deployment
- **Task #22:** Kubernetes Helm charts
- **Task #23:** Multi-cloud storage (S3, GCS, Azure Blob)
- **Task #24:** API server for embedding generation
- **Task #25:** Real-time documentation sync
- **Task #26:** Performance benchmarking suite
- **Task #27:** Production deployment guides
---
## Files Summary
### Created (2 files, ~800 lines)
- `src/skill_seekers/mcp/tools/vector_db_tools.py` (500+ lines)
- `tests/test_mcp_vector_dbs.py` (274 lines)
### Modified (3 files)
- `src/skill_seekers/mcp/tools/__init__.py` (+16 lines)
- `src/skill_seekers/mcp/server_fastmcp.py` (+140 lines)
- (Updated: tool count, imports, new section)
### Total Impact
- **New Lines:** ~800
- **Modified Lines:** ~150
- **Test Coverage:** 8/8 passing
- **New MCP Tools:** 4
- **MCP Tool Count:** 21 → 25
---
## Lessons Learned
### What Worked Well ✅
1. **Consistent patterns** - Following existing MCP tool structure made integration seamless
2. **Comprehensive testing** - 8 test cases caught all edge cases
3. **Clear documentation** - Usage instructions in output reduce support burden
4. **Error handling** - Graceful degradation for missing dependencies
### Challenges Overcome ⚡
1. **Async testing** - Converted to synchronous tests with asyncio.run() wrapper
2. **pytest-asyncio unavailable** - Used run_async() helper for compatibility
3. **Import paths** - Careful CLI_DIR path handling for adaptor access
---
## Quality Metrics
- **Test Pass Rate:** 100% (8/8)
- **Code Coverage:** All new functions tested
- **Documentation:** Complete docstrings and usage examples
- **Integration:** Seamless with existing MCP server
- **Performance:** Tests run in <0.5 seconds
---
**Task #19: MCP Server Integration for Vector Databases - COMPLETE ✅**
**Ready for Week 3 Task #20: GitHub Actions Automation**