Best AI Code Review Tools for Automated PR Reviews

Setup Guide

The following setup creates a functional AI code review environment in Claude Code. The same configuration works in Cursor with a path change.

Step 1: Create GitHub and Sentry API Tokens

• GitHub Personal Access Token — Create at github.com/settings/tokens. Required scopes:repo, pull_requests, issues
• Sentry Auth Token — Create at sentry.io under Settings › Auth Tokens. Required scopes:project:read, event:read, org:read

Step 2: Configure MCP Servers

// ~/.claude/settings.json
{
  "mcpServers": {
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_PERSONAL_ACCESS_TOKEN": "ghp_your_token_here"
      }
    },
    "sentry": {
      "command": "npx",
      "args": ["-y", "@sentry/mcp-server"],
      "env": {
        "SENTRY_AUTH_TOKEN": "sntrys_your_token_here",
        "SENTRY_ORG": "your-org-slug"
      }
    }
  }
}

Step 3: Create a Review Prompt

Save a reusable review prompt as a Claude Code slash command or a plain text template:

Review PR #$PR_NUMBER in $REPO:
1. Read the diff and list changed files
2. Identify any security anti-patterns or SQL injection risks
3. Check Sentry for errors related to the changed functions
4. Note any missing error handling or test coverage gaps
5. Post a structured review comment with: Summary, Issues (Critical/Medium/Low), Approval recommendation

Step 4: Automate with GitHub Actions (Optional)

For fully automated review on every PR, create a GitHub Actions workflow that calls your Claude agent via the Anthropic API when a pull request is opened or updated. The workflow passes the PR number and repository name as environment variables, and the agent posts the review programmatically via GitHub MCP.

Automated Review Workflow

The complete AI code review workflow moves a PR from opened to reviewed in under three minutes:

Stage 1: PR Opened

A developer pushes a branch and opens a pull request. The GitHub webhook fires, triggering the review workflow. No manual action is needed.

Stage 2: Agent Fetches Context

The AI agent uses GitHub MCP to retrieve the PR diff, changed file list, and any related issues. Simultaneously, it queries Sentry MCP for production errors in the affected modules and runs ESLint on the changed TypeScript files.

Stage 3: Analysis and Synthesis

The agent synthesizes all signals into a structured review:

• Security — Flags SQL injection, XSS, or insecure credential handling
• Reliability — Correlates changed code with existing Sentry errors
• Quality — Reports ESLint violations and missing type annotations
• Coverage — Notes functions that lack corresponding test changes

Stage 4: Review Comment Posted

The agent posts the review as a GitHub PR review comment with a clear verdict: Approved, Approved with suggestions, or Changes requested. Human reviewers see the AI review immediately and can focus their attention on the flagged items.

Stage 5: Approval and Merge

Once the developer addresses any blocking issues and the human reviewer confirms, the PR merges. The AI review comment becomes part of the PR history, creating an audit trail of automated and human review decisions.

When to Use or Skip AI Code Review

AI code review is strongest when the job is repeatable and evidence-based. It should not be used as a blind approval system for high-risk changes. Treat the agent as a reviewer assistant that collects signals, explains tradeoffs, and points humans toward the risky parts of a pull request.

Use AI code review when

• Your repository already has deterministic tests, lint rules, and CI status checks.
• The agent token can be scoped to read PRs and post comments without broad write access.
• Review comments need to be consistent across many small or medium pull requests.
• The team wants production error context from Sentry or similar systems during review.

Skip or delay AI code review when

• The repository contains regulated data, proprietary algorithms, or customer code without an approved data boundary.
• The agent would need administrator permissions or unrestricted repository write access.
• The team does not have a human owner for false positives, failed checks, or disputed comments.
• There are no tests or repeatable verification commands for the agent to cite.

Production Readiness Checklist

Before using AI review on a production repository, verify these controls. They make the difference between a useful reviewer assistant and an uncontrolled automation surface.

Evidence Checklist for Trustworthy AI Review

A high-quality AI code review does not simply say that a pull request looks risky. It shows why. Use this checklist before giving an agent persistent access to production repositories.

Comparison Table

Use this table to choose the right combination of skills for your team size and code quality requirements.

Frequently Asked Questions