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Open-Source MCP servers

Production-ready MCP servers that extend AI capabilities through file access, database connections, API integrations, and other contextual services.

10,543 servers. Last updated -

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Matching MCP servers:

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    Enhances AI model capabilities with structured, retrieval-augmented thinking processes that enable dynamic thought chains, parallel exploration paths, and recursive refinement cycles for improved reasoning.
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    MIT License
    • Apple
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    Kotlin MCP Server for Android app development using OpenAI, Gemini, or OpenRouter. Enables AI-assisted coding via Aider, Gradle build/test integration, Kotlin LSP, and Docker-based portability.
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    AGPL 3.0
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    A server that integrates Retrieval-Augmented Generation (RAG) with the Model Control Protocol (MCP) to provide web search capabilities and document analysis for AI assistants.
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    Apache 2.0
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    A Model Context Protocol server that enables Claude to execute Python code using boto3 to query and manage AWS resources directly from conversations.
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    Implements Retrieval-Augmented Generation (RAG) using GroundX and OpenAI, allowing users to ingest documents and perform semantic searches with advanced context handling through Modern Context Processing (MCP).
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    An MCP-compatible system that handles large files (up to 200MB) with intelligent chunking and multi-format document support for advanced retrieval-augmented generation.
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    MIT License

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    A complete MCP server for Retrieval-Augmented Generation with file management and vector memory for agents. Supports multiple document formats (PDF, DOCX, TXT, MD, CSV, JSON) with semantic search using Hugging Face embeddings and ChromaDB for efficient vector storage.
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    A server that implements Retrieval-Augmented Generation using GroundX and OpenAI, enabling semantic search and document retrieval with Modern Context Processing for enhanced context handling.
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    An MCP server that implements Retrieval-Augmented Generation to efficiently retrieve and process important information from various sources, providing accurate and contextually relevant responses.
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    A TypeScript MCP server that allows querying documents using LLMs with context from locally stored repositories and text files through a RAG (Retrieval-Augmented Generation) system.
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    An MCP server that provides comprehensive multimodal Retrieval-Augmented Generation (RAG) capabilities for processing and querying document directories, supporting text, images, tables, and equations.
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    An MCP server that enables RAG (Retrieval-Augmented Generation) on markdown documents by converting them to embedding vectors and performing vector search using DuckDB.
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    Apache 2.0
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    A BM25-based MCP server that enables document search and retrieval across structured domains of knowledge content, allowing Claude to search and reference documentation when answering questions.
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    A Python server that enables retrieval-augmented generation through semantic, question/answer, and style search modalities using PostgreSQL and pgvector for embedding storage and retrieval.
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    Apache 2.0
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    A Model Context Protocol server that exposes Retrieval-Augmented Generation capabilities and a weather tool, allowing clients to interact with document knowledge bases and retrieve weather information.
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    Connects a RAG application to open-webui using Model Context Protocol (MCP), enabling server-to-client communication for context retrieval and tool usage in remote environments through Server-Sent Events (SSE).
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    A Retrieval-Augmented Generation server that enables semantic PDF search with OCR capabilities, allowing users to query document content through any MCP client and receive intelligent answers.
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    An agentic AI system that orchestrates multiple specialized AI tools to perform business analytics and knowledge retrieval, allowing users to analyze data and access business information through natural language queries.
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    An advanced MCP server providing RAG-enabled memory through a knowledge graph with vector search capabilities, enabling intelligent information storage, semantic retrieval, and document processing.
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    Provides Retrieval-Augmented Generation capabilities by parsing documents (PDF, Markdown) and storing them in ChromaDB for semantic search and context retrieval.
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    Implements a RAG workflow that integrates with any custom knowledge base and can be triggered directly from the Cursor IDE.
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    Web crawling and RAG implementation that enables AI agents to scrape websites and perform semantic search over the crawled content, storing everything in Supabase for persistent knowledge retrieval.
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    Provides image generation capabilities for Claude using the Replicate Flux model, allowing users to create images from text prompts with customizable parameters like aspect ratio and output format.
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    Enables AI-powered image generation using Azure DALL-E 3 and FLUX models with intelligent automatic model selection. Generates stunning photorealistic or creative images directly within LibreChat through simple text prompts.
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    "primitive" RAG-like web search model context protocol server that runs locally. ✨ no APIs ✨
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    Enables AI applications to access and contextualize organizational knowledge sources including GitHub repositories and internal documentation through standardized MCP protocol integration. Features OAuth 2.1 authentication, vector-based semantic search, and optimized context chunking for enterprise development workflows.
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    Enables semantic search and contextual conversations with your Calibre ebook library using vector-based RAG technology. Supports project-based organization, multi-format book processing, and OCR capabilities for enhanced content extraction and retrieval.
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    Enables semantic search and retrieval of code files using embeddings stored in PostgreSQL. Supports intelligent codebase exploration through natural language queries, file listing, and content retrieval.
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    A lightweight server that provides persistent memory and context management for AI assistants using local vector storage and database, enabling efficient storage and retrieval of contextual information through semantic search and indexed retrieval.
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    Enables AI assistants to search and query PDF documents through a local RAG system with vector embeddings. Provides semantic document search capabilities while keeping all data stored locally without external dependencies.
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    Implementation of an MCP server for the RAG Web Browser Actor. This Actor serves as a web browser for large language models (LLMs) and RAG pipelines, similar to a web search in ChatGPT.
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    Apache 2.0
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    An MCP server that generates AI agent tools from Postman collections and requests. This server integrates with the Postman API to convert API endpoints into type-safe code that can be used with various AI frameworks.
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    MIT License