MCP server for Claude agent skills search via vector search

An open-source MCP server for searching and retrieving Claude agent skills using vector search.

Claude Skills MCP Server: Vector Search for Agent Skills

This repository provides an open-source MCP server specifically designed for efficient searching and retrieval of Claude agent skills. Leveraging the power of vector search, this tool enables developers to quickly locate relevant skills for their AI agents, streamlining the development process and enhancing agent capabilities. The server acts as a centralized index for your Claude agent skills, making them easily discoverable and accessible through advanced semantic search.

What it Does

The Claude Skills MCP Server indexes Claude agent skills using vector embeddings. When you need to find a specific skill or a set of skills that match a particular intent or functionality, the server performs a vector similarity search against its indexed database. This allows for semantic matching, meaning it can find skills that are conceptually related, even if the exact keywords aren't present in the query. The output is a ranked list of relevant skills, facilitating rapid integration into your AI agent projects.

Key Features

Vector Search Integration: Utilizes vector embeddings for semantic skill discovery.
MCP Server Architecture: Built as an MCP server for robust and scalable operation.
Claude Agent Skill Focus: Optimized for indexing and searching skills relevant to Claude agents.
Open-Source: Freely available and modifiable under an open-source license.
GitHub Hosted: Source code and documentation are readily accessible on GitHub.

Who it's For

This tool is intended for AI developers, researchers, and engineers working with Claude agents. If you are building AI agents that require dynamic access to a library of skills, or if you need to efficiently manage and discover existing agent functionalities, this MCP server will be invaluable. It's particularly useful for projects involving complex agent interactions, skill composition, and the need for intelligent skill selection based on contextual understanding. Developers seeking to enhance the reusability and discoverability of their agent skills will find this a powerful addition to their toolkit.