Semantic Search AI Agent Skills

This repository is an example of how to create a MCP server for Qdrant, a vector search engine. An official Model Context Protocol server for keeping and retrieving memories in the Qdrant vector search engine. It acts as a semantic memory layer on top of the Qdrant database. 1. qdrant-store - Store some information in the Qdrant database - information (string): Information to store - metadata (JSO

A Model Context Protocol (MCP) server that provides local vector database functionality using FAISS for Retrieval-Augmented Generation (RAG) applications. - Local Vector Storage: Uses FAISS for efficient similarity search without external dependencies - Document Ingestion: Automatically chunks and embeds documents for storage - Semantic Search: Query documents using natural language with sentence

What a repository knows about itself. rag-rat is a local repo-intelligence index and MCP server for coding agents. It keeps source files read-only, writes only its own SQLite database, and answers with provenance on every result — current source, the code graph, git/GitHub history, and durable, source-anchored repo memories that persist across sessions and agents. Every coding harness already has

An MCP server for semantic code search & navigation that helps AI agents work efficiently without burning through costly tokens. Instead of reading entire files, agents can search conceptually and jump directly to the specific functions, classes, and code chunks they need. - OpenAI API Key: Required for generating embeddings (local embedding support planned) - Git: Must be a git repository (respec