Code Analysis MCP Server

# 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 ```bash # 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` ```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: ```json { "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. ```json { "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. ```json { "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. ```json { "tool": "file_info", "arguments": { "path": "src/main.py" } } ``` ### 🔍 search_code Search for patterns in code files using regex. ```json { "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. ```json { "tool": "find_definition", "arguments": { "symbol": "MyClass", "directory": "./src", // optional "language": "python" // optional: python, javascript } } ``` ### 🏗️ analyze_structure Analyze the structure of a code file. ```json { "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. ```json { "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`: ```bash cp .env.example .env ``` 2. Edit `.env` and add your API keys: ```env 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: ```bash pip install openai anthropic ``` 4. Test LLM connectivity: ```bash ./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 ```python # 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