Code Review Server

Platform-agnostic AI-powered code review server with webhook support and MCP integration.

✨ Features

• 🔌 Platform Agnostic: Single webhook endpoint for GitHub, GitLab, Bitbucket, Azure DevOps
• 🤖 AI-Powered: Uses Groq (Llama 3.3), Claude, or GPT-4 for intelligent code review
• 💬 Multiple Comment Styles: Summary comments, inline comments, or both
• 🎯 Focused Analysis: Security, performance, bugs, code quality
• 🔧 MCP Tools: Manual code review via Claude Desktop or other MCP clients
• 🚀 Easy Integration: Simple pipeline configuration

🏗️ Architecture

Pipeline Webhook → MCP Server → Platform Detection → AI Review → Post Comments

📦 Installation

1. Clone and Setup

cd python
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt

2. Configuration

Copy .env.example to .env and configure:

# AI Provider (choose one)
GROQ_API_KEY=your_key
# or
ANTHROPIC_API_KEY=your_key
# or
OPENAI_API_KEY=your_key

# Platform Tokens
GITHUB_TOKEN=your_github_token
GITLAB_TOKEN=your_gitlab_token
BITBUCKET_USERNAME=your_username
BITBUCKET_APP_PASSWORD=your_password
AZURE_DEVOPS_PAT=your_azure_pat
AZURE_DEVOPS_ORG=https://dev.azure.com/your-org

Edit config.yaml for review preferences:

ai:
  provider: "groq"  # or "anthropic" or "openai"
  model: "llama-3.3-70b-versatile"  # Groq models
  # model: "claude-3-5-sonnet-20241022"  # Anthropic
  # model: "gpt-4-turbo-preview"  # OpenAI

review:
  comment_strategy: "both"  # summary, inline, both
  report_levels:
    - critical
    - high
    - medium
  block_on_critical: true

3. Run Server

python server.py

Server runs on http://localhost:8000

🔧 Pipeline Integration

Bitbucket Pipelines

See examples/bitbucket-pipelines.yml

pipelines:
  pull-requests:
    '**':
      - step:
          script:
            - curl -X POST $REVIEW_SERVER_URL/webhook ...

GitHub Actions

See examples/github-actions.yml

- name: Trigger AI Review
  run: |
    curl -X POST ${{ secrets.REVIEW_SERVER_URL }}/webhook ...

GitLab CI/CD

See examples/gitlab-ci.yml

ai-code-review:
  script:
    - curl -X POST $REVIEW_SERVER_URL/webhook ...

Azure Pipelines

See examples/azure-pipelines.yml

- script: |
    curl -X POST $(REVIEW_SERVER_URL)/webhook ...

🎯 MCP Tools (Manual Review)

Use from Claude Desktop or any MCP client:

1. Review Code

{
  "tool": "review_code",
  "arguments": {
    "code": "def login(username, password):\n    query = f\"SELECT * FROM users WHERE username='{username}'\"",
    "focus": ["security", "bugs"]
  }
}

2. Analyze Diff

{
  "tool": "analyze_diff",
  "arguments": {
    "diff": "--- a/file.py\n+++ b/file.py\n..."
  }
}

3. Security Scan

{
  "tool": "security_scan",
  "arguments": {
    "code": "your_code_here",
    "language": "python"
  }
}

📊 Review Output

Summary Comment Example

## 🤖 AI Code Review

**Score:** 7/10 ⚠️

### 📝 Summary
Good code structure but found some security concerns...

### 📊 Issues Found
- Total: **5**
- 🔴 Critical: **1**
- 🟠 High: **2**
- 🟡 Medium: **2**

### ⚠️ Important Issues

#### 🔴 SQL Injection Vulnerability
**Severity:** CRITICAL
**Location:** `auth.py` (Line 42)

Using string concatenation for SQL queries...

**Suggestion:**
> Use parameterized queries...

Inline Comments

Comments posted directly on the problematic code lines.

🔒 Security

• Webhook signature verification
• API token authentication
• Environment-based secrets
• No sensitive data logging

🚀 Deployment

Docker

docker build -t mcp-code-review .
docker run -p 8000:8000 --env-file .env mcp-code-review

Production

• Use reverse proxy (nginx, traefik)
• Enable HTTPS
• Set up logging and monitoring
• Configure rate limiting

🛠️ Development

# Install dev dependencies
pip install -r requirements.txt

# Run tests
pytest

# Format code
black .

📝 API Endpoints

• GET / - Health check
• POST /webhook - Universal webhook endpoint
• GET /mcp/sse - MCP Server-Sent Events endpoint

🤝 Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Submit a pull request

📄 License

MIT License

🆘 Support

For issues and questions:

• Open an issue on GitHub
• Check documentation
• Review examples

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