@MarcusJellinghaus
基本信息
MCP server providing code quality checks (pylint and pytest) with smart LLM-friendly prompts for analysis and fixes. Enables Claude and other AI assistants to analyze your code and suggest improvements.
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"mcp_server_code_checker_python": {
"command": "python",
"args": [
"-m",
"venv",
".venv"
]
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
MCP Code Checker is a Model Context Protocol (MCP) server that provides code quality checking operations for Python projects. It enables AI assistants like Claude, VSCode with GitHub Copilot, or other MCP-compatible clients to run pylint, pytest, and mypy checks within a specified project directory.
Install the server via pip (pip install git+https://github.com/MarcusJellinghaus/mcp-tools-py.git), then run it with the required --project-dir argument. Configure it in your MCP client (e.g., Claude Desktop) using the mcpServers JSON block, pointing either --python-executable or --venv-path to the environment where pytest, pylint, and mypy are installed.
pyproject.tomlMCP Code Checker provides a more focused, secure approach: only pylint, pytest, and mypy can be executed, all operations are scoped to the specified `project_dir`, and results are formatted and size-limited to reduce context load. The server also logs every tool call with parameters, timing, and results.
The server requires pytest, pylint, and mypy to be installed in the same Python environment. The `--python-executable` or `--venv-path` must point to that environment (typically the tool’s own venv, not the project’s runtime venv).
No. This server covers Python projects only. Support for other languages is planned for separate, dedicated MCP servers.
Logs can be written to the console by default or to a structured JSON file using `--log-file`. If not specified, a timestamped log file is created in `project_dir/logs/mcp_tools_py_{timestamp}.log`. Use `--console-only` to disable file logging.
The only required argument is `--project-dir`, pointing to the base directory of your project. Optional arguments include `--python-executable`, `--venv-path`, `--test-folder`, `--log-level`, and `--log-file`. Tool-specific parameters (like `target_directories`) can be passed at runtime.
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