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MCP server for Python code analysis with RUFF linting and VULTURE dead code detection

Project description

MCP Server Analyzer for Python 🐍🔍

SafeSkill 92/100

CI/CD Pipeline PyPI version Python 3.10+ Docker License: MIT Code Coverage AgentSeal MCP

A powerful Model Context Protocol (MCP) server that provides comprehensive Python code analysis using RUFF for linting and VULTURE for dead code detection. Perfect for AI assistants, IDEs, and automated code review workflows.

🚀 Quick Start

VS Code Integration (One-Click Install)

For quick installation, use one of the one-click install buttons below...

Install with UV in VS Code Install with UV in VS Code Insiders

Install with Docker in VS Code Install with Docker in VS Code Insiders

For manual installation, add the following JSON block to your User Settings (JSON) file in VS Code. You can do this by pressing Ctrl + Shift + P and typing Preferences: Open User Settings (JSON).

Optionally, you can add it to a file called .vscode/mcp.json in your workspace. This will allow you to share the configuration with others.

Note that the mcp key is needed when using the mcp.json file.

Using uvx (recommended):

{
  "mcp": {
    "servers": {
      "analyzer": {
        "command": "uvx",
        "args": ["mcp-server-analyzer"]
      }
    }
  }
}

Using Docker:

{
  "mcp": {
    "servers": {
      "analyzer": {
        "command": "docker",
        "args": ["run", "-i", "--rm", "ghcr.io/anselmoo/mcp-server-analyzer"]
      }
    }
  }
}

Universal Installation

# Install with uvx (recommended)
uvx install mcp-server-analyzer

# Install with pip
pip install mcp-server-analyzer

# Run with Docker
docker run ghcr.io/anselmoo/mcp-server-analyzer:latest

# Install from source
git clone https://github.com/anselmoo/mcp-server-analyzer.git
cd mcp-server-analyzer
uv sync --dev
uv run mcp-server-analyzer

📋 Features

  • 🔍 RUFF Analysis: Comprehensive Python linting with auto-fixes
  • 🧹 Dead Code Detection: Find unused imports, functions, and variables with VULTURE
  • 📊 Quality Scoring: Combined analysis with quality metrics
  • 🚀 FastMCP Framework: High-performance MCP server implementation
  • 🐳 Docker Ready: Multi-architecture containers with security signing
  • 🔒 Secure: All releases signed with Sigstore for supply chain security

📈 Analysis Examples

RUFF Linting Preview

See comprehensive linting analysis examples: 📋 RUFF Analysis Preview

VULTURE Dead Code Detection Preview

Explore dead code detection capabilities: 🧹 VULTURE Analysis Preview

🛠️ Available Tools

Tool Description Use Case
ruff-check Lint Python code with RUFF Style violations, potential errors
ruff-format Format Python code with RUFF Code formatting and consistency
ruff-check-ci CI/CD optimized RUFF output GitHub Actions, GitLab CI
vulture-scan Dead code detection Unused imports, functions, variables
analyze-code Combined RUFF + VULTURE analysis Complete code quality assessment

🔧 Configuration

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "analyzer": {
      "command": "uvx",
      "args": ["mcp-server-analyzer"]
    }
  }
}

Zed

Add to your Zed settings.json:

"context_servers": {
  "analyzer": {
    "command": "uvx",
    "args": ["mcp-server-analyzer"]
  }
}

🧪 Development

Prerequisites

  • Python 3.10+
  • uv (recommended) or pip
  • Docker (optional)

Setup

# Clone repository
git clone https://github.com/anselmoo/mcp-server-analyzer.git
cd mcp-server-analyzer

# Install dependencies
uv sync --dev

# Run tests
uv run pytest

# Run pre-commit hooks
uv tool run pre-commit run --all-files

# Build Docker image
docker build -t mcp-server-analyzer .

Testing

# Run all tests
uv run pytest tests/ -v

# Run with coverage
uv run pytest --cov=src/mcp_server_analyzer --cov-report=html

# Test specific functionality
uv run pytest tests/test_server.py::TestAnalyzer::test_ruff_analysis

📊 Quality Metrics

The server provides quality scoring based on:

  • RUFF Issues: Style violations, potential bugs, complexity metrics
  • Dead Code Detection: Unused imports, functions, variables
  • Combined Score: Weighted quality assessment (0-100)

🔒 Security

  • Signed Releases: All releases signed with Sigstore
  • Container Signing: Docker images signed with Cosign
  • Trusted Publishing: PyPI releases use GitHub OIDC trusted publishing
  • Vulnerability Scanning: Automated security scanning in CI/CD
  • Supply Chain Security: SLSA Build Level 3 compliance

📚 Documentation

🤝 Contributing

Contributions are welcome! Please see our Contributing Guide for details.

  1. Fork the repository
  2. Create a feature branch (git checkout -b feature/amazing-feature)
  3. Commit your changes (git commit -m 'Add amazing feature')
  4. Push to the branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments


Made with ❤️ for better Python code quality

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