Grafema MCP server

Grafema reads your project and builds a map of how everything connects: which function calls which, where data comes from, what depends on what. Once that map exists, your AI can ask questions about your code instead of opening files one by one. It is handy if you work in a codebase that has grown big enough that nobody remembers how it all fits together.

What this MCP server does

You install Grafema and run one command that scans your project and builds a graph of it. Then you tell your AI editor to use the Grafema MCP server. When you ask your AI something about your code, the AI sends that question to Grafema instead of reading files. Grafema looks it up in its graph and sends back the answer, which your AI turns into a normal reply for you. The result is usually faster and more complete than having the AI read files one by one.

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What you can do with it

• Find who calls a function across the whole project
• Trace where a value comes from and where it flows to
• Get a short overview of what a file contains
• Look up why a piece of code is the way it is, from linked notes
• Check whether your architectural rules are still respected
• Search for a node by an approximate name when you are not sure of the exact spelling
• Generate documentation or an API description from the live graph

Try asking your AI

• “Who calls handleRequest in this project?”
• “Where does req.user come from and where does it go?”
• “Give me a short overview of src/server.ts”
• “Why is the auth-middleware written this way?”
• “Are any of my architectural rules broken right now?”

What it gives back to you

You get answers in your chat: lists of callers, short file summaries, traces of where a value came from, or a yes/no on your rules with the spots that break them. Sometimes it returns a generated document or an API description you can save. The answers come from Grafema's map of your code, not from reading every file, so they can be quicker and cover more of the project.

Before you start

Who it's for

Developers and engineers working in large or unfamiliar codebases who want their AI to understand the structure of the project, not just the text of individual files.