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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.

9,457 servers. Last updated -

Matching MCP tools:

Matching MCP servers:

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    Model Context Protocol server to run Python code in a sandbox.
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    Enables secure execution of Python code in a sandboxed WebAssembly environment using Pyodide and Deno. Automatically handles package management and captures complete execution results including stdout, stderr, and return values.
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    Enables execution of Python code in a safe environment, including running scripts, installing packages, and retrieving variable values. Supports file operations and package management through pip.
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    Apache 2.0
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    A secure MCP server example that demonstrates how to deploy to Google Cloud Run with authentication and identity token protection. Serves as a tutorial template for building production-ready MCP servers in the cloud.
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    A Model Context Protocol server that allows LLMs to interact with Python environments, enabling code execution, file operations, package management, and development workflows.
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    Enables LLM agents to execute arbitrary Python code in secure, isolated sandbox environments with automatic dependency management and file handling. Each code execution runs in a fresh virtual environment that gets destroyed after completion, providing safe and reproducible Python code execution.
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    MIT License

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    A Model Context Protocol server that enables AI assistants like Claude to perform Python development tasks through file operations, code analysis, project management, and safe code execution.
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    A Model Context Protocol server that extracts and analyzes Python code structures, focusing on import/export relationships between files to help LLMs understand code context.
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    MIT License
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    A Model Context Protocol (MCP) server that allows AI models to safely access and interact with local file systems, enabling reading file contents, listing directories, and retrieving file metadata.
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    A server that provides a persistent Python REPL environment through the MCP protocol, allowing execution of Python code, variable management, and package installation.
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    An MCP server that executes tox commands to run Python tests within a project using pytest, allowing users to run all tests or specific test groups, files, cases, or directories.
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    Enables AI models to perform file system operations (reading, creating, and listing files) on a local file system through a standardized Model Context Protocol interface.
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    A collection of Python-based Model Context Protocol servers that extend AI assistant capabilities with tools for calculations, AWS services (S3 and RDS), and PostgreSQL database operations.
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    Provides an interactive Python REPL environment that maintains persistent session state, allowing users to execute Python code and access session history.
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    • Apple
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    Provides deep git file-level forensics tools to analyze detailed file histories, changes, and patterns within git repositories, focusing on individual file analysis rather than repository-wide operations.
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    Apache 2.0
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    A Model Context Protocol server that allows LLMs to interact with Python environments, execute code, and manage files within a specified working directory.
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    A secure proxy solution for hosting MCP (Model Context Protocol) servers on Google Cloud Run, allowing teams to share access through IAM authentication while bypassing current MCP authentication limitations.
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    A Model Context Protocol server that enables enhanced file system operations including reading, writing, copying, moving files with streaming capabilities, directory management, file watching, and change tracking.
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    This project implements a Model Context Protocol (MCP) server providing Formula One racing data using the Python FastF1 library. Inspired by an existing TypeScript server, it offers similar F1 data functionalities natively in Python via FastF1.
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    A basic example demonstrating how to develop and run a serverless Model Context Protocol (MCP) implementation using Netlify Functions and Express.
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    Automates file system operations through natural language commands using a combination of LLM (Ollama), MCP tools, and a Python bridge agent.
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    Enables MCP-compatible AI agents to deploy applications to Google Cloud Run by providing tools for deploying code, listing services, and managing Google Cloud projects.
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    Apache 2.0
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    A streamlined foundation for building Model Context Protocol servers in Python, designed to make AI-assisted development of MCP tools easier and more efficient.
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    A Python-based server allowing seamless integration with JIRA for managing and interacting with projects through custom APIs.
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    Apache 2.0
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    Enables merging multiple files into a single file through a simple MCP interface. Provides a secure way to combine files while restricting access to only allowed directories.
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    Apache 2.0
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    This server offers secure, read-only access to directory files via the MCP, supporting file searching, .gitignore compliance, path traversal protection, and MIME type detection using the 'file://' URI scheme.
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    MIT License
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    A Python implementation of the Model Context Protocol (MCP) server that enables searching and extracting information from arXiv papers, designed to be extensible with additional MCP tools.
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    An MCP server that enables users to fetch Python documentation using the Brave Search API through natural language queries.
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    A demonstration server showing MCP implementation in Python with resource handling, tool operations, and reusable prompts for a simple user/post system with local database.
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