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:
Claude Desktop Configuration
Add this to your Claude Desktop configuration:
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 forlimit
(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:
Database Schema
This tool expects a Turso database with the following schema:
The embedding
column should contain vector embeddings that can be
used with the vector_distance_cos
function.
Development
Setup
- Clone the repository
- Install dependencies:
- Build the project:
- Run in development mode:
Publishing
The project uses changesets for version management. To publish:
- Create a changeset:
- Version the package:
- Publish to npm:
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
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.
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.
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