MCP Code Checker

@MarcusJellinghaus

リポジトリ

基本情報

カテゴリ

開発者ツール

ライセンス

MIT

トランスポート

stdio

作者

MarcusJellinghaus

MCP Code Checker について

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.

#python #mcp #pytest#pylint#mcp-server

MCP Code Checker の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "mcp-code-checker": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        ".venv"
      ]
    }
  }
}

MCP Code Checker のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is MCP Code Checker?

MCP Code Checker is a Model Context Protocol server that enables AI assistants to run code quality checks on Python projects. It provides a focused set of tools – pylint, pytest, and mypy – scoped to a specified project directory, with structured output and actionable prompts to reduce context load.

How to use MCP Code Checker?

Install via pip from the GitHub repository, then run the mcp-tools-py command-line tool with the required --project-dir argument. Configure it as an MCP server in clients like Claude Desktop or VSCode using the mcp-config tool or manual JSON configuration. No additional setup beyond pointing to a virtual environment where pylint, pytest, and mypy are installed.

Key features of MCP Code Checker

Runs pylint, pytest, and mypy on Python projects
Auto-detects source and test directories from pyproject.toml
Customizable parameters for each tool (extra args, markers, strict mode)
Structured JSON logging with function call tracking
Output formatted as actionable prompts for AI assistants
Scoped to a single project directory for security

Use cases of MCP Code Checker

AI‑assisted code review that triggers linting and type checking
Automated test execution and failure analysis within a chat interface
Continuous integration style checks triggered from an MCP client
Developer debugging with detailed logs of each tool execution

よくある質問

How does this differ from giving an AI assistant bash access?

The server provides a more controlled environment: only pylint, pytest, and mypy can be run, all operations are confined to the specified project directory, and output is limited and formatted to reduce context consumption. This enhances security and transparency compared to a general-purpose bash MCP tool.

What are the runtime requirements?

The server requires Python and the tools (pylint, pytest, mypy) to be installed in a virtual environment. The `--python-executable` or `--venv-path` must point to that environment, not the project’s runtime venv. Installation is done via `pip install git+https://github.com/MarcusJellinghaus/mcp-tools-py.git`.

Where does the data live?

All operations are performed on the local file system within the specified `--project-dir`. No data is sent externally. Temporary files are created during test execution and cleaned up unless `--keep-temp-files` is used.

What are the known limitations?

Only Python projects are supported, and only the three built-in tools (pylint, pytest, mypy) are available. Additional languages would require separate, dedicated MCP servers.

How is the server accessed and authenticated?

The server uses standard MCP transport with no built-in authentication. It is intended for local or trusted network usage. Configuration is done via the client’s MCP settings file (e.g., `claude_desktop_config.json`).

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