"Retrieve the latest documentation of libraries in word embeddings form for LLMs" matching MCP servers:

Enables fast, token-efficient access to large documentation files in llms.txt format through semantic search. Solves token limit issues by searching first and retrieving only relevant sections instead of dumping entire documentation.

Enables semantic search across multiple Qdrant vector database collections, supporting multi-query capability and providing semantically relevant document retrieval with configurable result counts.

A Model Context Protocol server for generating text embeddings using OpenAI, Anthropic, or Ollama, with tools for single and batch embedding operations.

This server enables semantic search capabilities using Qdrant vector database and OpenAI embeddings, allowing users to query collections, list available collections, and view collection information.

Enables AI agents to intelligently search and retrieve Roblox documentation through semantic search and vector embeddings, providing natural language access to complete Roblox Creator Documentation.

Enables semantic search and retrieval of MCP (Model Context Protocol) documentation using Redis-backed embeddings, allowing users to query and access documentation content through natural language.

Provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.

An MCP server that provides AI assistants with access to Multi Theft Auto: San Andreas function documentation through vector similarity search and smart keyword expansion. It enables efficient information retrieval with features like deprecation warnings and SQLite caching for technical documentation.

Enables AI assistants to enhance their responses with relevant documentation through a semantic vector search, offering tools for managing and processing documentation efficiently.

An enterprise-ready MCP server that exposes a RAG tool for retrieving relevant context and metadata from a Qdrant vector database using natural language queries.

An Agent Framework Documentation server that enables AI agents to efficiently retrieve information from documentation databases using hybrid semantic and keyword search for seamless agent integration.

A Machine Control Protocol (MCP) server that enables storing and retrieving information from a Qdrant vector database with semantic search capabilities.

An integration server implementing the Model Context Protocol that enables LLM applications to interact with Milvus vector database functionality, allowing vector search, collection management, and data operations through natural language.

An MCP server that integrates with LangChain and ChromaDB to provide documentation search for AI libraries and vector database management.

Enables vector similarity search and serving of Svelte documentation via the MCP protocol, with support for local caching and multiple llms.txt documentation formats.

Provides semantic search capabilities over the Plesk Extensions Guide documentation using Retrieval-Augmented Generation (RAG) and vector embeddings. It enables AI assistants to retrieve relevant technical information and answer natural language queries regarding Plesk extension development.

Provides semantic search and complete document retrieval by ID through OpenAI's Vector Store API, letting deep research agents access company-specific knowledge and tools.

Enables semantic search and management of documentation through vector similarity using Qdrant and Ollama/OpenAI embeddings.

Enables semantic search and management of Zotero reference libraries using PostgreSQL with pg-vector and OpenAI/Ollama embeddings. Provides AI-powered search, full-text extraction, metadata access, annotations, notes, tags, and collections management.

Enables Claude to store and retrieve memories as 1024-dimensional embeddings in a local SQLite database using the Model Context Protocol.

A local RAG-powered documentation search system that uses vector embeddings and Qdrant to enable semantic search across markdown, HTML, and other file formats. It provides an MCP interface for AI tools like Cursor to intelligently query and retrieve information from local knowledge bases.

Enables real-time indexing and semantic search of local documents (PDF, Word, text, Markdown, RTF) using vector embeddings and local LLMs. Monitors folders for changes and provides natural language search capabilities through Claude Desktop integration.

Enables users to ingest documents into a PostgreSQL/pgvector knowledge base, run semantic search over them, and get grounded answers through a retrieval-augmented generation pipeline backed by a free LLM. It also lets clients spin up specialized AI agents on demand and exposes knowledge-base stats and configuration as resources for tutoring workflows.

Upload Word, Excel, PDF, or PowerPoint documents to a vector RAG store with vision-model extraction, then search semantically and retrieve chunks with page numbers for precise citations.

Enables semantic search and retrieval over local Markdown/MDX documentation using Node.js-based embeddings. Supports multi-language documentation with offline vector indexing and MCP tool exposure for AI assistants.

A local-first Graph-RAG system combining ChromaDB with metadata-based graph relationships and Gemini 2.5 Flash for intelligent Q&A over Obsidian vaults, supporting MCP clients like Claude Desktop, Cursor, and Raycast.

A server implementation that allows secure communication between MCP clients and privateGPT, enabling users to chat with privateGPT using knowledge bases and manage sources, groups, and users through a standardized Model Context Protocol.

Enables AI assistants to search and retrieve information from Teleport documentation using a vector database. It provides a tool for semantic vector search over pre-populated embeddings of Teleport pages and examples to assist with technical queries.

An MCP server implementation that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context. Uses Ollama or OpenAI to generate embeddings. Docker files included

An MCP server aimed to be portable, local, easy and convenient to support semantic/graph based retrieval of txtai "all in one" embeddings database. Any txtai embeddings db in tar.gz form can be loaded