🔍 Automated Code Review with AI Agents & MCP Servers
Build an automated code review pipeline using AI coding agents and MCP servers for GitHub pull requests. Integrates Cursor, Claude, and Git MCP servers.
🛠️ Tools Used in This Workflow
📝 Step-by-Step Guide
Step 1: Set Up GitHub MCP Server
Install the official GitHub MCP server to give your AI agent access to repositories, pull requests, and code diffs. Configure it with a GitHub personal access token that has repo scope. This allows the agent to read PR contents and post review comments.
Step 2: Configure Semgrep Security Scanner
Add the Semgrep MCP server to scan code for security vulnerabilities and anti-patterns. Semgrep provides over 2,000 rules covering OWASP Top 10, injection flaws, and language-specific issues. The MCP integration lets your agent run scans programmatically.
Step 3: Create the Review Agent Workflow
Connect Claude Code as the reasoning engine. When a new PR is opened, the workflow triggers: (1) GitHub MCP fetches the diff, (2) Semgrep MCP scans for vulnerabilities, (3) Claude analyzes code quality, logic errors, and suggests improvements, (4) Results are posted as PR review comments.
Step 4: Set Up Webhook Triggers
Configure a GitHub webhook to trigger the workflow on pull_request.opened and pull_request.synchronize events. Use n8n or a simple Express server as the webhook receiver. The workflow should batch-process files to stay within token limits.
Step 5: Test and Iterate
Submit a test PR with known issues (SQL injection, unused variables, missing error handling). Verify the agent catches them. Fine-tune the system prompt to reduce false positives and focus on high-impact findings.
💡 Use Cases
- Development teams wanting automated first-pass code reviews
- Open source maintainers managing high PR volume
- Security-conscious teams needing continuous vulnerability scanning
🔗 Related Tools
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