pyeye-marketplace

PyEye 👁️

PyEye Server

An extensible MCP (Model Context Protocol) server that provides intelligent Python code analysis, navigation, and understanding capabilities for AI assistants like Claude.

Features

• 🔍 Semantic Code Navigation: Find symbols, go to definitions, find references using Jedi
• 📊 Module & Package Analysis: List packages/modules, analyze dependencies, detect circular imports
• 🏗️ Multi-Project Support: Analyze multiple projects and dependencies simultaneously
• 📦 Namespace Packages: Handle packages distributed across multiple repositories
• 📝 Standalone Scripts: Analyze notebooks, scripts, and examples alongside formal packages
• 🔄 Auto-Update: Automatically detects and reflects file changes with smart cache invalidation
• ⚙️ Configuration System: Flexible configuration via files, env vars, or auto-discovery
• 🔌 Extensible Plugin System: Framework-specific analyzers (Pydantic, Django, Flask)
• 🚀 Fast & Cached: Intelligent caching with LRU eviction and performance optimization
• 🎯 Type-Aware: Full understanding of Python type hints and annotations
• 📈 Performance Monitoring: Built-in metrics tracking with p50/p95/p99 latencies
• 🛡️ Input Validation: Secure parameter validation and path checking
• 🤖 Development Automation: Release automation, dogfooding metrics, and worktree safety

Installation

The PyEye can be installed in two ways:

Option 1: Project-Specific Installation (Recommended)

Install directly into your Python project's virtual environment:

# Activate your project's virtual environment
source /path/to/your/project/venv/bin/activate

# Install from PyPI
pip install pyeye-mcp

# Or install from source
git clone https://github.com/okeefeco/pyeye-mcp.git
pip install -e ./pyeye-mcp

Then create a .mcp.json file in your project root:

{
  "mcpServers": {
    "pyeye": {
      "type": "stdio",
      "command": "python",
      "args": ["-m", "pyeye.mcp"],
      "env": {}
    }
  }
}

This way, the MCP server uses your project's environment and has access to all your project's dependencies.

Option 2: Global Installation

For analyzing multiple projects or using with global Python:

# Install globally with pipx (recommended for isolation)
pipx install pyeye-mcp

# Or with pip
pip install --user pyeye-mcp

# Or from source
git clone https://github.com/okeefeco/pyeye-mcp.git
cd pyeye-mcp
pip install --user .

Configure with Claude Code (Global)

# Add the MCP server globally (available in all projects)
claude mcp add pyeye -s user -- python -m pyeye.mcp

# Verify it's connected
claude mcp list

Configure with Claude Desktop

Add to your Claude Desktop configuration (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "pyeye": {
      "command": "python",
      "args": ["-m", "pyeye.mcp"],
      "env": {}
    }
  }
}

Note: Use the full path to Python if needed (e.g., /usr/local/bin/python3 or C:\\Python311\\python.exe).

Configure with GitHub Copilot (VS Code)

As of 2025, GitHub Copilot has full MCP support in VS Code, JetBrains, Eclipse, and Xcode. Follow these steps to use this PyEye server with GitHub Copilot:

Prerequisites

• GitHub Copilot Business or Enterprise subscription (required for MCP support)
• VS Code version 1.102 or later (MCP support is GA)
• Organization MCP policy enabled by your admin

Step 1: Enable MCP in Your Organization

Your GitHub Copilot administrator needs to enable the MCP servers policy:

1. Go to your organization settings on GitHub
2. Navigate to Copilot → Policies
3. Enable "MCP servers in Copilot" policy
4. Save changes

Step 2: Install the MCP Server

Install the PyEye server in your project or globally:

# Option A: Install in your project's virtual environment (recommended)
pip install pyeye-mcp

# Option B: Install globally with pipx
pipx install pyeye-mcp

# Option C: Install from source
git clone https://github.com/okeefeco/pyeye-mcp.git
pip install -e ./pyeye-mcp

Step 3: Configure VS Code