Building AI-Friendly Scientific Software: A Model Context Protocol Journey
In this post, I walk through building a remote Model Context Protocol (MCP) server that enhances AI agents’ ability to navigate and contribute meaningfully to the complex Napistu scientific codebase.
This tool empowers new users, advanced contributors, and AI agents alike to quickly access relevant project knowledge.
Before MCP, I fed Claude a mix of README files, wikis, and raw code hoping for useful answers. Tools like Cursor struggled with the tangled structure, sparking the idea for the Napistu MCP server.
I’ll cover:
- Why I built the Napistu MCP server and the problems it solves
- How I deployed it using GitHub Actions and Google Cloud Run
- Case studies showing how AI agents perform with — and without — MCP context