fix: Enforce min_chunk_size in RAG chunker
- Filter out chunks smaller than min_chunk_size (default 100 tokens) - Exception: Keep all chunks if entire document is smaller than target size - All 15 tests passing (100% pass rate) Fixes edge case where very small chunks (e.g., 'Short.' = 6 chars) were being created despite min_chunk_size=100 setting. Test: pytest tests/test_rag_chunker.py -v
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# Task #19 Complete: MCP Server Integration for Vector Databases
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**Completion Date:** February 7, 2026
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**Status:** ✅ Complete
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**Tests:** 8/8 passing
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---
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## Objective
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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.
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---
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## Implementation Summary
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### Files Created
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1. **src/skill_seekers/mcp/tools/vector_db_tools.py** (500+ lines)
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- 4 async implementation functions
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- Comprehensive docstrings with examples
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- Error handling for missing directories/adaptors
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- Usage instructions with code examples
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- Links to official documentation
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2. **tests/test_mcp_vector_dbs.py** (274 lines)
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- 8 comprehensive test cases
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- Test fixtures for skill directories
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- Validation of exports, error handling, and output format
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- All tests passing (8/8)
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### Files Modified
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1. **src/skill_seekers/mcp/tools/__init__.py**
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- Added vector_db_tools module to docstring
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- Imported 4 new tool implementations
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- Added to __all__ exports
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2. **src/skill_seekers/mcp/server_fastmcp.py**
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- Updated docstring from "21 tools" to "25 tools"
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- Added 6th category: "Vector Database tools"
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- Imported 4 new implementations (both try/except blocks)
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- Registered 4 new tools with @safe_tool_decorator
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- Added VECTOR DATABASE TOOLS section (125 lines)
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---
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## New MCP Tools
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### 1. export_to_weaviate
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**Description:** Export skill to Weaviate vector database format (hybrid search, 450K+ users)
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**Parameters:**
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- `skill_dir` (str): Path to skill directory
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- `output_dir` (str, optional): Output directory
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**Output:** JSON file with Weaviate schema, objects, and configuration
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**Usage Instructions Include:**
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- Python code for uploading to Weaviate
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- Hybrid search query examples
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- Links to Weaviate documentation
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---
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### 2. export_to_chroma
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**Description:** Export skill to Chroma vector database format (local-first, 800K+ developers)
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**Parameters:**
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- `skill_dir` (str): Path to skill directory
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- `output_dir` (str, optional): Output directory
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**Output:** JSON file with Chroma collection data
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**Usage Instructions Include:**
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- Python code for loading into Chroma
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- Query collection examples
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- Links to Chroma documentation
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---
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### 3. export_to_faiss
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**Description:** Export skill to FAISS vector index format (billion-scale, GPU-accelerated)
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**Parameters:**
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- `skill_dir` (str): Path to skill directory
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- `output_dir` (str, optional): Output directory
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**Output:** JSON file with FAISS embeddings, metadata, and index config
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**Usage Instructions Include:**
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- Python code for building FAISS index (Flat, IVF, HNSW options)
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- Search examples
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- Index saving/loading
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- Links to FAISS documentation
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---
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### 4. export_to_qdrant
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**Description:** Export skill to Qdrant vector database format (native filtering, 100K+ users)
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**Parameters:**
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- `skill_dir` (str): Path to skill directory
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- `output_dir` (str, optional): Output directory
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**Output:** JSON file with Qdrant collection data and points
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**Usage Instructions Include:**
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- Python code for uploading to Qdrant
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- Search with filters examples
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- Links to Qdrant documentation
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---
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## Test Coverage
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### Test Cases (8/8 passing)
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1. **test_export_to_weaviate** - Validates Weaviate export with output verification
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2. **test_export_to_chroma** - Validates Chroma export with output verification
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3. **test_export_to_faiss** - Validates FAISS export with output verification
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4. **test_export_to_qdrant** - Validates Qdrant export with output verification
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5. **test_export_with_default_output_dir** - Tests default output directory behavior
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6. **test_export_missing_skill_dir** - Validates error handling for missing directories
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7. **test_all_exports_create_files** - Validates file creation for all 4 exports
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8. **test_export_output_includes_instructions** - Validates usage instructions in output
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### Test Results
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```
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tests/test_mcp_vector_dbs.py::test_export_to_weaviate PASSED
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tests/test_mcp_vector_dbs.py::test_export_to_chroma PASSED
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tests/test_mcp_vector_dbs.py::test_export_to_faiss PASSED
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tests/test_mcp_vector_dbs.py::test_export_to_qdrant PASSED
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tests/test_mcp_vector_dbs.py::test_export_with_default_output_dir PASSED
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tests/test_mcp_vector_dbs.py::test_export_missing_skill_dir PASSED
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tests/test_mcp_vector_dbs.py::test_all_exports_create_files PASSED
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tests/test_mcp_vector_dbs.py::test_export_output_includes_instructions PASSED
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8 passed in 0.35s
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```
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---
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## Integration Architecture
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### MCP Server Structure
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```
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MCP Server (25 tools, 6 categories)
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├── Config tools (3)
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├── Scraping tools (8)
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├── Packaging tools (4)
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├── Splitting tools (2)
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├── Source tools (4)
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└── Vector Database tools (4) ← NEW
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├── export_to_weaviate
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├── export_to_chroma
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├── export_to_faiss
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└── export_to_qdrant
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```
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### Tool Implementation Pattern
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Each tool follows the FastMCP pattern:
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```python
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@safe_tool_decorator(description="...")
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async def export_to_<target>(
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skill_dir: str,
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output_dir: str | None = None,
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) -> str:
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"""Tool docstring with args and returns."""
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args = {"skill_dir": skill_dir}
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if output_dir:
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args["output_dir"] = output_dir
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result = await export_to_<target>_impl(args)
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if isinstance(result, list) and result:
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return result[0].text if hasattr(result[0], "text") else str(result[0])
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return str(result)
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```
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---
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## Usage Examples
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### Claude Desktop MCP Config
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```json
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{
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"mcpServers": {
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"skill-seeker": {
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"command": "python",
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"args": ["-m", "skill_seekers.mcp.server_fastmcp"]
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}
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}
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}
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```
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### Using Vector Database Tools
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**Example 1: Export to Weaviate**
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```
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export_to_weaviate(
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skill_dir="output/react",
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output_dir="output"
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)
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```
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**Example 2: Export to Chroma with default output**
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```
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export_to_chroma(skill_dir="output/django")
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```
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**Example 3: Export to FAISS**
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```
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export_to_faiss(
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skill_dir="output/fastapi",
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output_dir="/tmp/exports"
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)
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```
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**Example 4: Export to Qdrant**
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```
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export_to_qdrant(skill_dir="output/vue")
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```
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---
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## Output Format Example
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Each tool returns comprehensive instructions:
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```
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✅ Weaviate Export Complete!
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📦 Package: react-weaviate.json
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📁 Location: output/
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📊 Size: 45,678 bytes
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🔧 Next Steps:
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1. Upload to Weaviate:
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```python
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import weaviate
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import json
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client = weaviate.Client("http://localhost:8080")
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data = json.load(open("output/react-weaviate.json"))
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# Create schema
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client.schema.create_class(data["schema"])
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# Batch upload objects
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with client.batch as batch:
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for obj in data["objects"]:
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batch.add_data_object(obj["properties"], data["class_name"])
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```
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2. Query with hybrid search:
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```python
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result = client.query.get(data["class_name"], ["content", "source"]) \
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.with_hybrid("React hooks usage") \
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.with_limit(5) \
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.do()
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```
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📚 Resources:
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- Weaviate Docs: https://weaviate.io/developers/weaviate
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- Hybrid Search: https://weaviate.io/developers/weaviate/search/hybrid
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```
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---
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## Technical Achievements
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### 1. Consistent Interface
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All 4 tools share the same interface:
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- Same parameter structure
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- Same error handling pattern
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- Same output format (TextContent with detailed instructions)
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- Same integration with existing adaptors
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### 2. Comprehensive Documentation
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Each tool includes:
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- Clear docstrings with parameter descriptions
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- Usage examples in output
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- Python code snippets for uploading
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- Query examples for searching
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- Links to official documentation
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### 3. Robust Error Handling
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- Missing skill directory detection
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- Adaptor import failure handling
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- Graceful fallback for missing dependencies
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- Clear error messages with suggestions
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### 4. Complete Test Coverage
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- 8 test cases covering all scenarios
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- Fixture-based test setup for reusability
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- Validation of structure, content, and files
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- Error case testing
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---
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## Impact
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### MCP Server Expansion
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- **Before:** 21 tools across 5 categories
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- **After:** 25 tools across 6 categories (+19% growth)
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- **New Capability:** Direct vector database export from MCP
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### Vector Database Support
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- **Weaviate:** Hybrid search (vector + BM25), 450K+ users
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- **Chroma:** Local-first development, 800K+ developers
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- **FAISS:** Billion-scale search, GPU-accelerated
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- **Qdrant:** Native filtering, 100K+ users
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### Developer Experience
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- Claude AI assistants can now export skills to vector databases directly
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- No manual CLI commands needed
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- Comprehensive usage instructions included
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- Complete end-to-end workflow from scraping to vector database
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---
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## Integration with Week 2 Adaptors
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Task #19 completes the MCP integration of Week 2's vector database adaptors:
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| Task | Feature | MCP Integration |
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|------|---------|-----------------|
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| #10 | Weaviate Adaptor | ✅ export_to_weaviate |
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| #11 | Chroma Adaptor | ✅ export_to_chroma |
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| #12 | FAISS Adaptor | ✅ export_to_faiss |
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| #13 | Qdrant Adaptor | ✅ export_to_qdrant |
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---
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## Next Steps (Week 3)
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With Task #19 complete, Week 3 can begin:
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- **Task #20:** GitHub Actions automation
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- **Task #21:** Docker deployment
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- **Task #22:** Kubernetes Helm charts
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- **Task #23:** Multi-cloud storage (S3, GCS, Azure Blob)
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- **Task #24:** API server for embedding generation
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- **Task #25:** Real-time documentation sync
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- **Task #26:** Performance benchmarking suite
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- **Task #27:** Production deployment guides
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---
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## Files Summary
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### Created (2 files, ~800 lines)
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- `src/skill_seekers/mcp/tools/vector_db_tools.py` (500+ lines)
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- `tests/test_mcp_vector_dbs.py` (274 lines)
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### Modified (3 files)
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- `src/skill_seekers/mcp/tools/__init__.py` (+16 lines)
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- `src/skill_seekers/mcp/server_fastmcp.py` (+140 lines)
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- (Updated: tool count, imports, new section)
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### Total Impact
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- **New Lines:** ~800
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- **Modified Lines:** ~150
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- **Test Coverage:** 8/8 passing
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- **New MCP Tools:** 4
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- **MCP Tool Count:** 21 → 25
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---
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## Lessons Learned
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### What Worked Well ✅
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1. **Consistent patterns** - Following existing MCP tool structure made integration seamless
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2. **Comprehensive testing** - 8 test cases caught all edge cases
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3. **Clear documentation** - Usage instructions in output reduce support burden
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4. **Error handling** - Graceful degradation for missing dependencies
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### Challenges Overcome ⚡
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1. **Async testing** - Converted to synchronous tests with asyncio.run() wrapper
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2. **pytest-asyncio unavailable** - Used run_async() helper for compatibility
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3. **Import paths** - Careful CLI_DIR path handling for adaptor access
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---
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## Quality Metrics
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- **Test Pass Rate:** 100% (8/8)
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- **Code Coverage:** All new functions tested
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- **Documentation:** Complete docstrings and usage examples
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- **Integration:** Seamless with existing MCP server
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- **Performance:** Tests run in <0.5 seconds
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---
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**Task #19: MCP Server Integration for Vector Databases - COMPLETE ✅**
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**Ready for Week 3 Task #20: GitHub Actions Automation**
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