# 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

1. **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

2. **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

1. **src/skill_seekers/mcp/tools/__init__.py**
   - Added vector_db_tools module to docstring
   - Imported 4 new tool implementations
   - Added to __all__ exports

2. **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 directory
- `output_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 directory
- `output_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 directory
- `output_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 directory
- `output_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)

1. **test_export_to_weaviate** - Validates Weaviate export with output verification
2. **test_export_to_chroma** - Validates Chroma export with output verification
3. **test_export_to_faiss** - Validates FAISS export with output verification
4. **test_export_to_qdrant** - Validates Qdrant export with output verification
5. **test_export_with_default_output_dir** - Tests default output directory behavior
6. **test_export_missing_skill_dir** - Validates error handling for missing directories
7. **test_all_exports_create_files** - Validates file creation for all 4 exports
8. **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:

```python
@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

```json
{
  "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"])
   ```

2. Query with hybrid search:
   ```python
   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**