MCP Embedding Search

# mcp-embedding-search A Model Context Protocol (MCP) server that queries a Turso database containing embeddings and transcript segments. This tool allows users to search for relevant transcript segments by asking questions, without generating new embeddings. ## Features - 🔍 Vector similarity search for transcript segments - 📊 Relevance scoring based on cosine similarity - 📝 Complete transcript metadata (episode title, timestamps) - ⚙️ Configurable search parameters (limit, minimum score) - 🔄 Efficient database connection pooling - 🛡️ Comprehensive error handling - 📈 Performance optimized for quick responses ## Configuration This server requires configuration through your MCP client. Here are examples for different environments: ### Cline Configuration Add this to your Cline MCP settings: ```json { "mcpServers": { "mcp-embedding-search": { "command": "node", "args": ["/path/to/mcp-embedding-search/dist/index.js"], "env": { "TURSO_URL": "your-turso-database-url", "TURSO_AUTH_TOKEN": "your-turso-auth-token" } } } } ``` ### Claude Desktop Configuration Add this to your Claude Desktop configuration: ```json { "mcpServers": { "mcp-embedding-search": { "command": "node", "args": ["/path/to/mcp-embedding-search/dist/index.js"], "env": { "TURSO_URL": "your-turso-database-url", "TURSO_AUTH_TOKEN": "your-turso-auth-token" } } } } ``` ## API The server implements one MCP tool: ### search_embeddings Search for relevant transcript segments using vector similarity. Parameters: - `question` (string, required): The query text to search for - `limit` (number, optional): Number of results to return (default: 5, max: 50) - `min_score` (number, optional): Minimum similarity threshold (default: 0.5, range: 0-1) Response format: ```json [ { "episode_title": "Episode Title", "segment_text": "Transcript segment content...", "start_time": 123.45, "end_time": 167.89, "similarity": 0.85 } // Additional results... ] ``` ## Database Schema This tool expects a Turso database with the following schema: ```sql CREATE TABLE embeddings ( id INTEGER PRIMARY KEY AUTOINCREMENT, transcript_id INTEGER NOT NULL, embedding TEXT NOT NULL, FOREIGN KEY(transcript_id) REFERENCES transcripts(id) ); CREATE TABLE transcripts ( id INTEGER PRIMARY KEY AUTOINCREMENT, episode_title TEXT NOT NULL, segment_text TEXT NOT NULL, start_time REAL NOT NULL, end_time REAL NOT NULL ); ``` The `embedding` column should contain vector embeddings that can be used with the `vector_distance_cos` function. ## Development ### Setup 1. Clone the repository 2. Install dependencies: ```bash npm install ``` 3. Build the project: ```bash npm run build ``` 4. Run in development mode: ```bash npm run dev ``` ### Publishing The project uses changesets for version management. To publish: 1. Create a changeset: ```bash npm run changeset ``` 2. Version the package: ```bash npm run version ``` 3. Publish to npm: ```bash npm run release ``` ## Contributing Contributions are welcome! Please feel free to submit a Pull Request. ## License MIT License - see the [LICENSE](LICENSE) file for details. ## Acknowledgments - Built on the [Model Context Protocol](https://github.com/modelcontextprotocol) - Designed for efficient vector similarity search in transcript databases