kandrwmrtn/cplusplus_mcp

An MCP (Model Context Protocol) server for analyzing C++ codebases using libclang.
Why Use This?
Instead of having Claude grep through your C++ codebase trying to understand the structure, this server provides semantic understanding of your code. Claude can instantly find classes, functions, and their relationships without getting lost in thousands of files. It understands C++ syntax, inheritance hierarchies, and call graphs - giving Claude the ability to navigate your codebase like an IDE would.
Features
Context-efficient C++ code analysis:
- **search_classes** - Find classes by name pattern
- **search_functions** - Find functions by name pattern
- **get_class_info** - Get detailed class information (methods, members, inheritance)
- **get_function_signature** - Get function signatures and parameters
- **find_in_file** - Search symbols within specific files
- **get_class_hierarchy** - Get complete inheritance hierarchy for a class
- **get_derived_classes** - Find all classes that inherit from a base class
- **find_callers** - Find all functions that call a specific function
- **find_callees** - Find all functions called by a specific function
- **get_call_path** - Find call paths from one function to another
Prerequisites
- Python 3.9 or higher
- pip (Python package manager)
- Git (for cloning the repository)
- LLVM's libclang (the setup scripts will attempt to download a portable build)
Setup
1. Clone the repository:
2. Run the setup script for your platform (this creates a virtual environment, installs dependencies, and fetches libclang if possible):
- **Windows**
- **Linux/macOS**
3. Test the installation (recommended):
This will verify that all components are properly installed and working. The test script lives at scripts/test_installation.py.
Configuring Claude Code
To use this MCP server with Claude Code, you need to add it to your Claude configuration file.
1. Find and open your Claude configuration file. Common locations include:
The exact location may vary depending on your Claude installation.
2. Add the C++ MCP server to the mcpServers section:
**IMPORTANT:** Replace YOUR_INSTALLATION_PATH_HERE with the actual path where you cloned this repository.
3. Restart Claude Desktop for the changes to take effect.
Configuring Codex CLI
To use this MCP server inside the OpenAI Codex CLI:
1. Make sure the virtual environment is created (see setup above).
2. Create a .mcp.json file in the project you open with Codex. The CLI reads this file to discover MCP servers.
3. Add an entry that points to the Python module inside the virtual environment. Replace YOUR_REPO_PATH with the absolute path to this repository.
On Windows change command to YOUR_REPO_PATH\\mcp_env\\Scripts\\python.exe.
4. Restart the Codex CLI (or run codex reload) so it picks up the new server definition.
5. Inside Codex, use the MCP palette or prompt instructions (for example, "use the cpp-analyzer tool to set the project directory to ...") to start indexing your C++ project.
If you keep the .mcp.json file inside this repository you can also add a "cwd": "YOUR_REPO_PATH" entry so Codex launches the server from the correct directory.
Usage with Claude
Once configured, you can use the C++ analyzer in your conversations with Claude: