With the emergence and rise of Model Context Protocol (MCP), developers are discovering revolutionary ways for AI and agents to interact with tools, codebases, and even browsers.  

Building on top of the core technology, we are seeing projects, such as browser extensions and tools within code editors, enabling AI-native workflows and unlocking a new category of agentic tooling: innovative ecosystems and new projects focused on MCP-powered capabilities are changing the way we work. 

In partnership with the Microsoft Open Source Program Office (OSPO), the GitHub Copilot and VS Code teams sponsored nine projects to accelerate innovation, security, and sustainability within open source. Below you’ll find the projects and the three major themes we’re seeing across their work.

Framework and platform integrations: Ecosystem integrations for real-world use cases  

These projects integrate bring MCP capabilities into popular frameworks and ecosystems for AI-native tooling and help MCP with widely used platforms, and enable agents to interact with real-world apps and workflows: 

fastapi_mcp: Expose secure FastAPI endpoints as MCP tools with minimal setup, authentication, and limited configuration—all with a unified infrastructure. 

nuxt-mcp: Nuxt developer tools for route inspection and SSR debugging make it easier for your team to make models understand your Vite/Nuxt app better. 

unity-mcp: Unity MCP allows you to interface with game engine APIs for AI-assisted game development and gives your AI tools to manage assets, control scenes, edit scripts, and automate tasks within Unity 

Developer experience and AI-enhanced coding: AI-first developer productivity  

These projects empower AI,  LLMs and agents to act as intelligent IDE assistants and code editors by improving developer workflows, semantic code understanding, and safe code execution.

context7: Context7 pulls up-to-date, version-specific documentation and code examples straight from your code and plugs them directly into your AI and LLM prompts LLM’s context.  

serena: Semantic code editing and retrieval for agent-driven coding agent toolkit providing semantic retrieval and editing capabilities.  

Peekaboo: Swift code analysis that turns what’s on your screen into actionable AI context to create full GUI automation, and can be used for AI assistants.  

coderunner: Coderunner turns LLMs into an instant, local execution partner that writes and runs code in a preconfigured sandbox on your machine, auto-installs tools, directly reads files, and returns outputs and generated artifacts. 

Automation, testing and orchestration: Reliability and quality assurance for MCP infrastructure 

These projects help extend MCP infrastructure into production grade tools for automation pipelines and providing robust testing, and debugging tools. These help ensure you can run MCP at scale. 

 MCP server evaluation: 

n8n-mcp: n8n-MCP is an ultra-optimized platform that enhances n8n’s workflow automation by streamlining workflow creation and orchestration. It integrates AI models to help users better understand and work with n8n nodes.

inspector: A tool for testing and debugging MCP servers by inspecting protocol handshake, tools, resources, prompts, and OAuth flows. It offers a built-in LLM playground and lets you run eval simulations to catch security or performance regressions.  

AI workflows and agentic developer productivity with MCP and open source 

Developers are building at incredible speed with the power of AI and MCP. These projects represent some of the fastest growing developer tools within the MCP ecosystem and community. They are tools that developers use and care about. GitHub Copilot and VS Code teams are excited to sponsor more open source projects that drive new innovations like MCP for agent-native development. 

Sign up for GitHub Sponsors today to join us in sponsoring these projects (and more!) and help support the MCP ecosystem. You can also start exploring MCP with VS Code and GitHub Copilot today!

Written by

Kevin Crosby

Senior Director, Open Source Funding