Originally published on the Dromeas blog.
Your coding agent writes code. MCP is how you give it a reviewer. Here's what the protocol actually does — and a working setup you can copy in minutes.
MCP, in one paragraph
The Model Context Protocol is an open standard that lets an AI client — Claude Code, Cursor, Windsurf, Zed — call external tools through a uniform interface. Instead of pasting output between your terminal and a review dashboard, the agent invokes a tool like review_pull_request directly and receives the result as structured data it can reason about. Tool support, not copy-paste, is what makes an agentic loop possible: the agent can act, observe the verdict, and repair — the loop we described in loop engineering.
Why a review layer belongs in the loop
A coding agent with no review layer grades its own homework. It writes a diff, checks that it compiles, and declares victory. A review layer changes the economics: the same agent submits its diff, gets an independent multi-model verdict — security, quality, compliance — and fixes what it finds before a human spends a minute on it. Self-review before the PR is the single highest-leverage place to insert checking, because the cost of a fix there is one tool call, not a review round-trip.
The setup, step by step
- Connect your repositories. Sign up at dromeas.ai and connect GitHub, GitLab or Bitbucket. Dromeas builds a typed code map of your repos so review verdicts come with real context.
- Get your MCP endpoint. Open Settings > MCP in the Dromeas app and copy your workspace's MCP server URL and API key. One endpoint covers Claude Code, Cursor, Windsurf, Zed and any other MCP-capable client.
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Add the server to your client. In Claude Code run
claude mcp addwith the URL; in Cursor, add the server under Settings > MCP. The review tools appear in your agent's tool list immediately. -
Ask your agent to self-review. Before opening a PR, ask your coding agent to submit the diff for review. It calls
review_pull_requestorreview_local_diff, gets a multi-model verdict, and can fix what it finds before a human ever looks.
What your agent can actually do once connected
The Dromeas MCP server exposes the full review surface as tools: review_pull_request and review_local_diff for verdicts, code_finder_search and the code-map tools for cheap context before edits, get_findings and the fix tools for acting on results, and release tools like describe_release_state for go/no-go decisions. The point is that review isn't a dashboard your agent can't see — it's a function it can call.
Because every tool call is scoped to your workspace and logged, you keep the audit trail that matters when agents start merging on their own. That's the same provenance property the CISO checklist depends on.
Connect a repo, copy your MCP endpoint, and your coding agent gets a six-agent review council it can call from the terminal. Read about loop engineering for the fuller picture of what the loop looks like end to end.
Top comments (1)
The implementation of the Model Context Protocol (MCP) as a review layer is a powerful way to enhance the reliability of AI-generated code. By automating the self-review process before human intervention, you're effectively reducing the potential for introducing bugs and improving code quality significantly. It might be worth considering additional customizability in the review verdicts, perhaps allowing developers to prioritize certain aspects based on their team's unique requirements. If you’re looking for help fine-tuning this protocol or its integration into specific workflows, I’d be glad to explore a paid collaboration. What challenges have you faced in ensuring the accuracy of the verdicts from the different models?