Code Analysis MCP Server

A modular MCP (Model Context Protocol) server for code analysis with file operations, code search, and structure analysis capabilities.

Features

📁 File Operations

• read_file: Read contents of any code file
• list_files: List files in directories with pattern matching
• file_info: Get detailed file information (size, type, line count)

🔍 Code Search

• search_code: Search for patterns in code using regex
• find_definition: Find symbol definitions (functions, classes, variables)

📊 Code Analysis

• analyze_structure: Analyze code structure (imports, classes, functions)

Installation

# Clone the repository
git clone https://github.com/yourusername/code-mcp.git
cd code-mcp

# Create virtual environment
python -m venv venv

# Activate environment
source venv/bin/activate  # On Unix/macOS
venv\Scripts\activate     # On Windows

# Install dependencies
pip install -r requirements.txt

Usage

1. With Claude Desktop

Add to your Claude Desktop configuration file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "code-analyzer": {
      "command": "python",
      "args": ["/absolute/path/to/code-mcp/server.py"]
    }
  }
}

Then restart Claude Desktop.

2. With Continue.dev (VS Code)

Add to your Continue configuration:

{
  "models": [...],
  "mcpServers": {
    "code-analyzer": {
      "command": "python",
      "args": ["/absolute/path/to/code-mcp/server.py"]
    }
  }
}

3. With Other MCP Clients

Any MCP-compatible client can use this server by pointing to the server.py file.

Available Tools

📖 read_file

Read the contents of a file.

{
  "tool": "read_file",
  "arguments": {
    "path": "src/main.py",
    "encoding": "utf-8"  // optional, default: utf-8
  }
}

📂 list_files

List files in a directory with optional pattern matching.

{
  "tool": "list_files",
  "arguments": {
    "directory": "./src",      // optional, default: current dir
    "pattern": "*.py",         // optional, default: *
    "recursive": true          // optional, default: false
  }
}

ℹ️ file_info

Get detailed information about a file.

{
  "tool": "file_info",
  "arguments": {
    "path": "src/main.py"
  }
}

🔍 search_code

Search for patterns in code files using regex.

{
  "tool": "search_code",
  "arguments": {
    "pattern": "def.*test",        // regex pattern
    "directory": "./src",          // optional
    "file_pattern": "*.py",        // optional
    "case_sensitive": false        // optional, default: true
  }
}

🎯 find_definition

Find where a symbol is defined.

{
  "tool": "find_definition",
  "arguments": {
    "symbol": "MyClass",
    "directory": "./src",          // optional
    "language": "python"           // optional: python, javascript
  }
}

🏗️ analyze_structure

Analyze the structure of a code file.

{
  "tool": "analyze_structure",
  "arguments": {
    "path": "src/main.py",
    "include_docstrings": true     // optional, default: false
  }
}

🤖 update_with_architecture

Compare old and new architecture versions and intelligently update the new file.

{
  "tool": "update_with_architecture",
  "arguments": {
    "old_file": "src/legacy/module.py",    // Reference file (old architecture)
    "new_file": "src/modern/module.py",    // Target file (will be updated)
    "backup": true                         // optional, default: true
  }
}

AI Configuration

To use the AI-powered tools, you need to configure your API keys:

1. Copy .env.example to .env:

   cp .env.example .env
   

2. Edit .env and add your API keys:

   AI_PROVIDER=openai
   OPENAI_API_KEY=your-openai-api-key
   # or
   AI_PROVIDER=anthropic  
   ANTHROPIC_API_KEY=your-anthropic-api-key
   

Thinking Models Support

The tool automatically handles "thinking" models (like o1, o1-preview) that include reasoning in their responses:

• Thinking sections are automatically removed
• Only the actual code is extracted
• Supports various thinking formats: <think>, [thinking], etc.

3. Install AI dependencies:

   pip install openai anthropic
   

4. Test LLM connectivity:

   ./test_llm.sh
   # or
   python tests/test_llm.py
   

Examples

In Claude Desktop

After configuring, you can ask Claude:

• "Read the file src/main.py"
• "Search for all functions that contain 'test' in the src directory"
• "Find where the class 'UserModel' is defined"
• "Analyze the structure of app.py"
• "List all Python files in the project"

Programmatic Usage

# Example of calling tools programmatically
import asyncio
from mcp import Client

async def main():
    client = Client()
    
    # Read a file
    result = await client.call_tool("read_file", {
        "path": "src/main.py"
    })
    
    # Search for patterns
    result = await client.call_tool("search_code", {
        "pattern": "TODO|FIXME",
        "directory": "./",
        "recursive": True
    })
    
    # Analyze structure
    result = await client.call_tool("analyze_structure", {
        "path": "src/main.py",
        "include_docstrings": True
    })

asyncio.run(main())

Architecture

The server follows a modular architecture:

├── server.py          # Main MCP server
├── tools/             # Tool definitions
│   ├── file_tools.py  # File operations
│   └── code_tools.py  # Code analysis tools
├── handlers/          # Request handlers
│   ├── file_handler.py
│   ├── search_handler.py
│   └── analyze_handler.py
└── core/              # Core services
    ├── file_system.py # File system operations
    └── code_parser.py # Code parsing logic

Supported Languages

• Python (.py)
• JavaScript/TypeScript (.js, .ts, .jsx, .tsx)
• Java (.java)
• C/C++ (.c, .cpp, .h)
• Go (.go)
• Rust (.rs)
• Ruby (.rb)
• And more...

Security

• File access is restricted to prevent directory traversal
• Large files are handled efficiently with streaming
• Search results are limited to prevent memory issues

Contributing

Feel free to submit issues and enhancement requests!

License

MIT