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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.

9,463 servers. Last updated -

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Matching MCP servers:

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    A Model Context Protocol server that fetches up-to-date, version-specific documentation and code examples from libraries directly into LLM prompts, helping developers get accurate answers without outdated or hallucinated information.
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    Fetches up-to-date, version-specific documentation and code examples from the source and adds them to your LLM prompts, helping eliminate outdated code generations and hallucinated APIs.
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    Share code context with LLMs via MCP or clipboard
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    Claude Context is an MCP plugin that adds semantic code search to Claude Code and other AI coding agents, giving them deep context from your entire codebase.
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    CTX is a tool made to solve a big problem when chatting with LLMs like ChatGPT or Claude: giving them enough context about your project.
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    Give your AI agents access to 8,500+ community curated awesome lists with over 1 million curated resources.
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    This MCP server provides an AI-powered productivity suite that connects Todo, Idea, Journal, and Timer apps with AI
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    A static MCP server that helps AI models maintain tool context across chat sessions, preventing loss of important information and keeping conversations smooth and uninterrupted.
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    Apache 2.0
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    MCP server to provide Sketch layout information to AI coding agents like Cursor
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    MCP server to provide Jira Tickets information to AI coding agents like Cursor
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    Provides semantic code search capabilities that run 100% locally using EmbeddingGemma embeddings. Enables finding code by meaning across 15 file extensions and 9+ programming languages without API costs or sending code to the cloud.
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    A CloudFlare Workers-based MCP server that provides semantic memory and journal capabilities with vector search. Enables users to store, search, and retrieve memories and journal entries using AI-powered semantic similarity without any local setup required.
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    An MCP server that provides an interface for querying the AtherOS knowledge base through an API, allowing users to create chat sessions and send queries to retrieve information.
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    This server provides an API to query Large Language Models using context from local files, supporting various models and file types for context-aware responses.
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    Provides persistent tool context that survives across Claude Desktop chat sessions, automatically injecting tool-specific rules, syntax preferences, and best practices. Eliminates the need to re-establish context in each new conversation.
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    A Model Context Protocol server that provides web content fetching capabilities with robots.txt checking removed, allowing LLMs to retrieve and convert web content to markdown.
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    A persistent memory layer for Claude Code that maintains project information, technology stack, tasks, decisions, and session history between coding sessions, eliminating the need to re-explain project context.
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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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    A simple MCP server that can enable MCP clients to query Bluesky instances.
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    A simple MCP server implementation in TypeScript that communicates over stdio, allowing users to ask questions that end with 'yes or no' to trigger the MCP tool in Cursor.
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    A platform that transforms AI development with intelligent context management, optimization, and prompt engineering, enabling developers to enhance model performance through structured context management and optimization tools.
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    The URL-Context-MCP MCP Server provides a tool to analyze and summarize the content of URLs using Google Gemini's URL Context capability via the Gemini API. Now also supports optional grounding with Google Search alongside URL Context. The server is designed to follow prompt-only orchestration: con
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    MCP-ORTools integrates Google's OR-Tools constraint programming solver with Large Language Models through the MCP, enabling AI models to: Submit and validate constraint models Set model parameters Solve constraint satisfaction and optimization problems Retrieve and analyze solution
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    Provides intelligent context management for AI development sessions, allowing users to track token usage, manage conversation context, and seamlessly restore context when reaching token limits.
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    A development platform that helps AI developers optimize prompts through intelligent context management, providing tools for analysis, optimization, and template management to reduce costs and improve AI response quality.
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    Provides AI coding assistants with context optimization tools including targeted file analysis, intelligent terminal command execution with LLM-powered output extraction, and web research capabilities. Helps reduce token usage by extracting only relevant information instead of processing entire files and command outputs.
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    A Model Context Protocol server that transforms text into structured JSON data using templates with placeholders.
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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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    An MCP server that provides semantic search over local git repositories, enabling users to clone repositories, process branches, and search code through vectorized code chunks.
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    An MCP server implementation that maximizes Gemini's 2M token context window with tools for efficient context management and caching across multiple AI client applications.
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