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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.

9,443 servers. Last updated -

Matching MCP tools:

Matching MCP servers:

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    An MCP server that runs AI-driven venture capitalist agents whose thinking is continuously enriched by Octagon Private Markets' real-time deals and intelligence for pitch feedback, diligence simulations, and term sheet negotiations.
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    MIT License
    • Apple
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    A lightweight framework for building and orchestrating AI agents through the Model Context Protocol, enabling users to create scalable multi-agent systems using only configuration files.
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    MIT License
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    A Model Context Protocol server that enables Claude users to access specialized OpenAI agents (web search, file search, computer actions) and a multi-agent orchestrator through the MCP protocol.
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    An intelligent MCP server that analyzes projects and recommends team compositions from 100+ professional roles across company departments, with automatic agent downloading for specialized tasks.
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    An intelligent MCP server that analyzes projects and recommends professional role assignments, enabling users to download specialized agent templates for 100+ roles across various company departments.
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    An MCP-based trivia game server where AI creates rounds with two true statements and one false 'twist' about various topics, letting players guess which statement is false.
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    5
    MIT License
    • Apple
    • Linux

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    A Python-based MCP server that allows Claude and other LLMs to execute arbitrary Python code directly through your desktop Claude app, enabling data scientists to connect LLMs to APIs and executable code.
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    Facilitates interaction and context sharing between AI models using the standardized Model Context Protocol (MCP) with features like interoperability, scalability, security, and flexibility across diverse AI systems.
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    A Model Context Protocol server that enables conversational LLMs to delegate complex research tasks to specialized AI agents powered by various OpenRouter models, coordinated by a Claude orchestrator.
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    A server that exposes OpenAI agents (web search, file search, computer actions, and multi-agent orchestration) through the Model Context Protocol, making them accessible to any MCP client including Claude Desktop.
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    Enables access to agent instruction files and prompts for AI development workflows. Provides tools to retrieve and list development rules, security checks, and common prompts from an agents library through MCP protocol.
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    Apache 2.0
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    A Model Context Protocol (MCP) server that enables integration of Strands agents with Amazon Q and other MCP-compatible systems through a plugin architecture.
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    MIT License
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    • Apple
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    Handles SQL query execution for a natural language interface to SQLite databases, enabling users to interact with databases using plain English rather than writing SQL manually.
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    A state-based agent orchestration system that allows transitions between different states (IDLE, PLANNING, RESEARCHING, EXECUTING, REVIEWING, ERROR) while maintaining conversation context and providing state-specific prompts.
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    Enables execution of code in a sandbox environment with integrated web search capabilities. Provides a basic framework for running code safely, primarily designed for AI agents and research applications.
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    A Model Context Protocol server providing LLMs read-only access to PostgreSQL databases for inspecting schemas and executing queries.
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    Enables web and local business searches through the Brave Search API. Provides general web search with pagination and filtering, plus local business search with automatic fallback to web results.
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    Integrates Claude Desktop with Azure AI Search, allowing users to query search indexes using keyword, vector, or hybrid search methods.
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    A Model Context Protocol server that converts SVG code to PNG images, offering two conversion methods (CairoSVG and Inkscape) with support for custom working directories.
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    Securely connects AI agents to multiple databases simultaneously while enabling collaborative learning from team query patterns, all while keeping data private by running locally.
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    A Redis-backed MCP server that enables multiple AI agents to communicate, coordinate, and collaborate while working on parallel development tasks, preventing conflicts in shared codebases.
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    Multi-Agent Mesh Network MCP server that spawns specialized AI agents working in parallel to solve complex coding problems, providing 3.64x faster performance through distributed intelligence.
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    MCP for working with GraphQL servers.
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    A simple TypeScript MCP server that provides two tools: one for greeting users with a customizable name and another for adding two numbers together.
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    A Model Context Protocol server for coordinating multiple parallel agents working on the same project within an IDE, managing projects, tasks, todo items, and file locking to enable safe concurrent development.
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    AGPL 3.0
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    A powerful MCP server that enables parallel Google searching with multiple keywords simultaneously, providing structured results while handling CAPTCHAs and simulating user browsing patterns.
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    A lightweight, centralized control plane server that enables management of multiple Kubernetes clusters simultaneously, supporting context switching and CRUD operations on common Kubernetes resources.
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    Apache 2.0
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    An MCP server that enables interaction with multiple Kubernetes clusters simultaneously, providing comprehensive tools for cluster management, resource operations, and diagnostics across different environments.
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    A lightweight short-term memory MCP server that automatically stores and recalls working context, session state, and task progress for AI agents. Memories auto-expire after 24 hours and integrate seamlessly with workspace-aware storage across multiple projects.
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    A server that enables communication with multiple unichat-based MCP servers simultaneously, allowing users to query different language models and combine their responses for more comprehensive results.
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