AI Prompt to Review Pull Requests for Bugs and Security Using GitHub's MCP Server
This prompt gives developers and engineering leads a repeatable way to review pull requests for bugs, security issues, and style problems by connecting an AI model to GitHub's MCP server, so it reads the actual diff, file list, and CI status instead of relying on pasted code snippets. It's built for developers, tech leads, and reviewers who want a consistent first pass on incoming pull requests before a human review.
With the MCP connection live, the model can pull the real diff for a specific PR number, check it against the team's house rules (naming conventions, error handling, test coverage expectations), and flag security-sensitive patterns like unsanitized input or hardcoded secrets. Because the review runs against the actual repository state rather than a copy-pasted chunk, the output stays accurate even as the PR gets updated with new commits.
Before wiring this into a review workflow, it helps to tighten the instructions themselves — running the prompt through Prompt Optimizer's Coding mode first will tighten vague review criteria into the specific checks a model can reliably apply.
Prompt template
Want it sharper? Optimize this prompt with Prompt Optimizer, check it with the Prompt Debugger or shorten it with the Token Optimizer.
Example input
When to use it
- Reviewing an incoming pull request before assigning it to a human reviewer, to catch obvious issues early
- Auditing a backlog of open PRs for security-sensitive patterns like hardcoded credentials or unvalidated input
- Checking a PR against team-specific style and testing conventions before merge
- Giving a junior developer automated feedback on their PR before it goes to a senior reviewer
Best practices
- Scope the review to one PR or one file set at a time rather than asking the model to review an entire repository at once
- Give the model your team's actual lint rules and PR checklist instead of asking for generic "best practices"
- Ask for inline, file-and-line-referenced feedback rather than a general summary, so comments map back to the diff
- Re-run the review after new commits are pushed to the same PR, since the diff changes
Common mistakes
- Pasting a static copy of the diff into the prompt instead of letting the MCP connection pull the live version, so feedback goes stale after new commits
- Asking the model to approve or merge the PR directly instead of treating its output as a first-pass review for a human to confirm
- Not specifying the project's language version or framework, leading to suggestions that don't match the actual codebase
- Treating every flagged issue as equally severe instead of asking the model to separate blocking issues from minor style notes
FAQs
How do I connect ChatGPT or Claude to a GitHub MCP server for code review?
You connect through an MCP-compatible client (such as Claude Desktop, Claude Code, or an MCP-enabled ChatGPT integration) that has a GitHub MCP server configured with a personal access token or GitHub App credentials. Once connected, the model can call tools to read repository contents, pull requests, and CI status directly instead of relying on text you paste in.
Can AI actually read a live pull request diff instead of a pasted code snippet?
Yes, when it's connected through an MCP server with GitHub access, the model can request the current diff, file list, and check status for a specific PR number in real time. This means the review reflects the latest commits on that PR rather than a snapshot you copied earlier.
Is an AI code review from an MCP-connected prompt a replacement for human review?
No. It's best used as a first pass that catches obvious bugs, security issues, and style violations before a human reviewer looks at the PR, not as a substitute for a maintainer's sign-off, especially for security-sensitive or architecturally significant changes.
Which Cuelara tool can help me tighten this prompt before using it on a real PR?
Prompt Optimizer — its Coding mode is built to turn vague review instructions into specific, checkable criteria, which is useful for sharpening the constraints in this exact prompt before you run it against a real pull request.