Which integrations are available for this server?

Provides vector similarity search capabilities for transcript segments stored in a [Turso](/mcp/servers/integrations/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

Related MCP server: Better Qdrant MCP Server

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

Clone the repository
Install dependencies:

npm install

Build the project:

npm run build

Run in development mode:

npm run dev

Publishing

The project uses changesets for version management. To publish:

Create a changeset:

npm run changeset

Version the package:

npm run version

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

Built on the Model Context Protocol
Designed for efficient vector similarity search in transcript databases

This server cannot be installed

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security - not tested

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license - not found

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quality - not tested

How are these scores calculated?

Resources

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