Show HN: A library to convert+deploy existing agent projects as MCP servers

22 points

a year ago

Most of the MCP servers that I’ve seen are tools implemented in standalone projects. To onboard more tools (especially agents and multi-agent workflows) to MCP, I’ve been thinking it’s important to allow AI engineers to continue to prototype in their existing agent frameworks and deploy with minimal conversion when ready.

We created the automcp library, which you can add as a dependency to existing projects (CrewAI, LangGraph, Llama Index, OpenAI Agents SDK, Pydantic AI, mcp-agent currently supported but more coming soon). You just need to run a CLI command to create a run_mcp.py file, make some edits and run it to start the server locally. You can think of run_mcp.py like Heroku’s Procfile, Codespaces configs, Pulumi/AWS CDK style IaC.

We also created a demo of a deployment platform where you can enter the GitHub URL of your project, deploy with one click, and get a URL for the hosted sse server that can be used with MCP clients like Cursor. Think of it like Vercel for MCP servers.

There are still a few manual steps for the user that can be further automated, but curious to hear whether people think it’s useful? There are some interesting directions automcp could go in in future like automatically creating MCP servers for each orchestrator, agent and tool in a project (rather than one monolithic MCP server).

Website: https://auto-mcp.com automcp repo: https://github.com/NapthaAI/automcp Deployment platform: https://labs.naptha.ai/ Demo: https://www.youtube.com/watch?v=El5YvBQ5py0

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