"How to use GitHub" matching MCP servers:

Semantic memory for AI builders: capture the tacit engineering know-how that never reaches your docs, recall it the moment it applies. Built in Rust on Postgres and pgvector.

RAG MCP server for PDFs, YouTube, GitHub repos, and Discord exports. Index documents and query with citations via LangChain and Chroma.

Turns a tools.yaml contract into typed, tenant-guarded vector database tools that agents can use via Python imports or MCP.

Provides fast static embedding, similarity, and search capabilities via MCP tools and an OpenAI-compatible HTTP API using a tiny 16M-parameter English embedding model.

Indexes codebases using semantic embeddings for natural language search, enabling developers to find code with queries like 'how does authentication work'.

Enables making REST API calls to Teradata cloud services including Elastic Compute, Vector Store, OMS, and QueryGrid. Supports custom authentication, Socks5 proxy, and multipart file uploads.

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Enables GitHub Copilot to query local ChromaDB instances to retrieve relevant documents and context for AI conversations. It allows users to search vector collections using natural language tools directly within VS Code.

An MCP server for providing semantically related Ethereum Improvement Proposals (EIPs) to AI agents.

Enables AI assistants to interact with Meilisearch through a standardized interface, supporting index and document management, search capabilities, settings configuration, task monitoring, and experimental vector search.

A lightweight RAG system that provides an MCP server for searching and interacting with vector-based knowledge bases. It enables users to perform retrieval-augmented generation and search across Qdrant collections through a standardized interface.

Vectorize MCP server for advanced retrieval, Private Deep Research, Anything-to-Markdown file extraction and text chunking.

Pinecone Assistant MCP server

An interface for managing and querying MariaDB databases that supports standard SQL operations alongside advanced vector and embedding-based search capabilities. It enables AI assistants to seamlessly integrate relational and vector data workflows through a standardized protocol.

MCP server for compressing AI embeddings by 5-7x using TurboQuant (PolarQuant + QJL), with tools to compress, decompress, estimate savings, and embed+compress vectors.

An MCP server that enables semantic search over a private knowledge base via pgvector, with GitHub OAuth authentication and a RAG pipeline.

An MCP server that gives AI assistants the ability to remember user information (preferences, behaviors) across conversations using vector search technology.

A server component of the Model Context Protocol that provides intelligent analysis of codebases using vector search and machine learning to understand code patterns, architectural decisions, and documentation.

A cloud-based vector memory service that provides AI assistants with persistent storage, semantic search, and entity management via the Model Context Protocol. It features multi-tenant isolation and bidirectional synchronization with macOS and Google contacts and calendars.

Enables MCP clients to remember user preferences and behaviors across conversations using vector search technology.