MCP Server Analyzer for Python 🐍🔍

A powerful Model Context Protocol (MCP) server that provides comprehensive Python code analysis using Ruff for linting, ty for type checking, 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...

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

• 🧠 ty Type Checking: Fast Python type analysis with rule-based diagnostics

• 🧹 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

🔧 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"]
  }
}

Claude Code (project-level)

Place .mcp.json at your project root:

{
  "mcpServers": {
    "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 type checks
uv run ty check src tests

# 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::TestAnalyzers::test_ruff_with_sample_code

📊 Quality Metrics

The server provides quality scoring based on:

• Ruff Issues: Style violations, potential bugs, complexity metrics

• ty Diagnostics: Static typing errors and warnings

• 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

• Security Policy: See SECURITY.md for vulnerability reporting

🔍 Data Handling & Transparency

• In-memory only: Code passed to tools is written to a temporary file, analyzed, and the file is deleted immediately — nothing is persisted between calls.

• No network calls: The server makes no outbound network connections during analysis.

• No telemetry: No usage data, analytics, or crash reports are collected.

• Subprocess isolation: ruff, ty, and vulture are invoked with fixed argument lists — no shell expansion or arbitrary command execution.

📚 Documentation

• Full Documentation - GitHub Pages docs

• Tools Reference - Detailed tool parameters and return types

• MCP Specification - Learn about Model Context Protocol

• FastMCP Framework - High-performance MCP implementation

• Ruff Documentation - Python linter and formatter

• ty Documentation - Python type checker and language server

• Vulture Documentation - Dead code finder

🤝 Contributing

Contributions are welcome! Please see CONTRIBUTING.md for details.

1. Fork the repository

2. Create a feature branch (git checkout -b feature/amazing-feature)

3. Commit your changes using Conventional Commits

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

• Astral for RUFF and uv

• Jendrik Seipp for VULTURE

• Model Context Protocol team

• FastMCP framework

Made with ❤️ for better Python code quality