Embedding Search
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.
What is Embedding Search?
Embedding Search is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to 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 embeddin...
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.
This server falls under the Search & Data Extraction category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- A Model Context Protocol server that searches transcript seg
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx mcp-embedding-searchConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
Frequently Asked Questions about Embedding Search
What is Embedding Search?
Embedding Search is a Model Context Protocol (MCP) server that 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. It connects AI assistants to external tools and data sources through a standardized interface.
How do I install Embedding Search?
Follow the installation instructions on the Embedding Search GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with Embedding Search?
Embedding Search works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is Embedding Search free to use?
Yes, Embedding Search is open source and available under the MIT license. You can use it freely in both personal and commercial projects.
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Browse More Search & Data Extraction MCP Servers
Explore all search & data extraction servers available in the MCPgee directory. Each server includes setup guides for Claude, Cursor, and VS Code.
Set Up Embedding Search in Your Editor
Choose your AI client for step-by-step setup instructions.
Quick Config Preview
Add this to your claude_desktop_config.json or .cursor/mcp.json
Ready to use Embedding Search?
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