MCP Embedding Search

by spences10
Verified

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

Integrations

  • Provides vector similarity search capabilities for transcript segments stored in a Turso database, retrieving relevant content based on natural language queries without generating new embeddings

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:

{ "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:

{ "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:

[ { "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:

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:
npm install
  1. Build the project:
npm run build
  1. Run in development mode:
npm run dev

Publishing

The project uses changesets for version management. To publish:

  1. Create a changeset:
npm run changeset
  1. Version the package:
npm run version
  1. Publish to npm:
npm run release

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT License - see the LICENSE file for details.

Acknowledgments

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security - not tested
F
license - not found
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quality - not tested

A Model Context Protocol server that searches transcript segments in a Turso database using vector similarity, allowing users to find relevant content by asking questions without generating new embeddings.

  1. Features
    1. Configuration
      1. Cline Configuration
        1. Claude Desktop Configuration
        2. API
          1. search_embeddings
          2. Database Schema
            1. Development
              1. Setup
                1. Publishing
                2. Contributing
                  1. License
                    1. Acknowledgments