A Curated List of Open Source MCP Projects and Tools

The Model Context Protocol (MCP) enables AI agents to interact with tools, codebases, and applications in a standardized way. This document lists ten notable open-source projects that demonstrate the expanding MCP ecosystem and provide frameworks for building AI-native, agentic workflows.

1. Framework and Platform Integrations

These projects integrate MCP capabilities into popular frameworks, allowing agents to interact with real-world applications and development workflows.

• fastapi_mcp: Exposes secure FastAPI endpoints as MCP tools with minimal setup, handling authentication and configuration with a unified infrastructure.
• nuxt-mcp: Provides Nuxt developer tools for route inspection and server-side rendering (SSR) debugging, helping models better understand Vite/Nuxt applications.
• unity-mcp: Creates an interface with Unity game engine APIs for AI-assisted game development. It gives AI tools the ability to manage assets, control scenes, edit scripts, and automate tasks within Unity.

2. Developer Experience and AI-Enhanced Coding

These projects focus on improving developer productivity by empowering LLMs and agents to act as intelligent IDE assistants.

• context7: Pulls up-to-date, version-specific documentation and code examples directly from a codebase and injects them into an LLM’s context for more accurate prompting.
• serena: A toolkit for agent-driven coding that provides semantic code retrieval and editing capabilities, moving beyond simple text matching.
• Peekaboo: A Swift code analysis tool that translates on-screen GUI elements into actionable AI context, enabling full GUI automation for AI assistants.
• coderunner: Turns an LLM into a local execution partner. It writes and runs code in a preconfigured sandbox, auto-installs dependencies, reads files, and returns outputs or generated artifacts.
• MCP CLI: A lightweight, standalone command-line interface (CLI) for interacting with MCP servers. It is designed specifically for AI coding agents to solve the “context window bloat” problem by enabling dynamic, just-in-time discovery of tools rather than loading all schemas upfront. See the full guide: MCP CLI: Dynamic Tool Discovery for AI Agents.

3. Automation, Testing, and Orchestration

These projects provide production-grade infrastructure for testing, debugging, and running MCP-based automation pipelines at scale.

• n8n-mcp: An optimized platform that enhances n8n’s workflow automation. It integrates AI models to help users create, orchestrate, and understand n8n nodes and workflows more effectively.
• inspector: A tool for testing and debugging MCP servers. It allows inspection of the protocol handshake, tools, resources, prompts, and OAuth flows. It includes a built-in LLM playground and supports evaluation simulations to catch security or performance regressions.