Why I automate code review with MCP

Code review is essential, but a lot of it is the same boring checklist: did you run the tests, fix the linter errors, and keep the types happy? That work is important, but it doesn’t need a human every single time.

I set up a custom MCP server for Cursor so those checks run automatically in my editor. By the time someone else looks at a pull request, we’re already past “your linter failed” and into the interesting parts: architecture, trade‑offs, and naming.

What the MCP server does

At a high level, the MCP server acts like a very opinionated assistant that understands my project and knows which commands to run.

It:

Runs the project’s linter and type checker, reporting errors by file and line directly in Cursor.
Triggers tests (e.g. Jest) and surfaces failures without leaving the IDE.
Optionally runs format checks so code style is consistent before the PR ever exists.

Instead of remembering half a dozen npm scripts, I invoke one MCP tool and get a clear, structured summary of what’s broken and where.

All of this happens inside Cursor, wired into the files I’m already editing. I see problems early, fix them in place, and push only when the build is effectively “green” locally.

Why it helps

For me, the value shows up in three places:

Faster feedback: I don’t have to push, wait for CI, and then circle back. The MCP server tells me immediately when something breaks, so the fix is still fresh in my head.
Better reviews: Reviewers can assume “lint, types, and tests pass” and spend their attention on architecture, readability, and long‑term maintenance instead of mechanical nits.
Easier onboarding: New contributors don’t need to memorize the project’s scripts. They run the same MCP tools I do and get consistent, guided feedback from day one.

In other words, the computer is strict about rules, and humans get to be generous about design and intent.

How I wired it up (at a high level)

Under the hood, the setup is straightforward:

The MCP server exposes tools like run_lint, run_types, and run_tests.
Each tool wraps the project’s existing commands (for example, npm run lint) and parses their output into a structured format.
Cursor calls these tools and decorates the editor with the results, so I can jump directly to offending lines.

I didn’t have to change my stack to get this working; I just formalized the checks I was already running into a repeatable, machine‑driven flow.

If you’re already using Cursor and you have a Node/TypeScript project, a small MCP server like this is a modest one‑time investment that keeps paying off every time you open a pull request.