Open-Source MCP servers

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

4,733 servers. Last updated -

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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
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    An open-source platform for Retrieval-Augmented Generation (RAG). Upload documents and query them ⚡
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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 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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    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 server that enables users to retrieve information from AWS Knowledge Bases using RAG (Retrieval-Augmented Generation) via Bedrock Agent Runtime.
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    A version-aware Kubernetes documentation assistant that connects LLMs to trusted, real-time Kubernetes docs to reduce hallucinations and ensure accurate, version-specific responses.
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    Enables semantic search and RAG (Retrieval Augmented Generation) over your Apple Notes.
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    This server provides access to the Perplexity AI API, enabling interaction through chatting, searching, and documentation retrieval within MCP-based systems.
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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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    Connects Bear Notes to AI assistants using semantic search and RAG (Retrieval-Augmented Generation), allowing AI systems to access and understand your personal knowledge base through meaningful search rather than just keyword matching.
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    A TypeScript implementation of a Model Context Protocol server that provides marketing tools based on Open Strategy Partners' methodologies, enabling content creation, optimization, and product positioning through tools like value map generation, meta information creation, and content editing.
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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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    Provides a standardized way to integrate Perplexity AI's features like chat, search, and documentation access into MCP-based systems.
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    A multi-model platform that integrates RAG (Retrieval-Augmented Generation) with LLMs, supporting OCR via Tesseract and offering both backend API and frontend web interface.
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    A Model Context Protocol server for querying and analyzing Azure resources at scale using Azure Resource Graph, enabling AI assistants to explore and monitor Azure infrastructure.
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    Used to create an assistant integrated with n8n that can search n8n documentation, example workflows, and community forums.
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    This server enables users to download entire websites and their assets for offline access, supporting configurable depth and concurrency settings.
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    The server facilitates access to Julia documentation and source code through Claude Desktop, allowing users to retrieve information on Julia packages, modules, types, functions, and methods.
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    A Machine Conversation Protocol server that enables AI agents to perform Retrieval-Augmented Generation by querying a FAISS vector database containing Sui Move language documents.
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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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    This server enables AI assistants (CLINE, Cursor, Windsurf, Claude Desktop) to share a common knowledge base through Retrieval Augmented Generation (RAG), providing consistent information access across multiple tools.
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    A Model Context Protocol implementation that enables AI assistants to interact with markdown documentation files, providing capabilities for document management, metadata handling, search, and documentation health analysis.
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    Enhances Claude AI with persistent memory storage for Infrastructure-as-Code components, supporting version tracking and relationship mapping for Terraform and Ansible resources.
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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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    A Model Context Protocol server that combines DeepSeek R1's reasoning capabilities with Claude 3.5 Sonnet's response generation, enabling two-stage AI processing where DeepSeek's structured reasoning enhances Claude's final outputs.
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    An MCP Server that integrates with Stability AI's API to provide high-quality image generation, editing, and manipulation capabilities including background removal, outpainting, search-and-replace, and upscaling.
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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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    Enables interaction with the Metal Framework by providing documentation search and code generation capabilities using natural language queries.
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    A remote MCP server providing a tool for searching and querying ATprotocol documentation, helping developers easily access information while building on the protocol.
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    Creative Commons Zero v1.0 Universal
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    Transform your non-existent or unreadable docs into an intelligent, searchable knowledge base that actually answers those 'basic questions' before they're asked.
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    Provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.
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    • Apple