MCP Tools Py

A Model Context Protocol (MCP) server providing code quality checking operations with easy client configuration. This server offers an API for performing code quality checks within a specified project directory, following the MCP protocol design.

Overview

This MCP server enables AI assistants like Claude (via Claude Desktop), VSCode with GitHub Copilot, or other MCP-compatible clients to run code quality checks on Python projects. The tools provided are:

• Run pylint checks to identify code quality issues

• Execute pytest to identify failing tests

• Run mypy for type checking

Scope: This server covers Python projects only. Further Python-specific extensions are planned, including architecture and layering checks (vulture, tach, import-linter) and refactoring tools. Support for other languages can be provided through separate, dedicated MCP servers with similar functionality.

Why a dedicated MCP server instead of bash access?

A general-purpose bash MCP tool allows more flexibility, but at the expense of less control. This server takes a more focused approach:

• Security: Only a defined set of tools (pylint, pytest, mypy) can be executed. All operations are scoped to the specified project_dir.

• Context management: Results are formatted and size-limited to reduce context load on the AI assistant. Output is structured as actionable prompts rather than raw tool output.

• Transparency: The server is open source, and detailed structured logging records every tool call with parameters, timing, and results.

Features

• run_pylint_check: Run pylint on the project code and generate smart prompts for LLMs

• run_pytest_check: Run pytest on the project code and generate smart prompts for LLMs

• run_mypy_check: Run mypy type checking on the project code

Pylint Parameters

The pylint tools expose the following parameters for customization:

Pylint Configuration

Pylint reads your project's pyproject.toml automatically. Control which issues are reported by configuring [tool.pylint.messages_control] in your pyproject.toml. See docs/pyproject-configuration.md for examples and migration guidance.

Target Directories Examples:

• ["src"] - Analyze only source code directory

• ["src", "tests"] - Analyze both source and test directories (default)

• ["mypackage", "tests"] - For projects with different package structures

• ["lib", "scripts", "tests"] - For complex multi-directory projects

• ["."] - Analyze entire project directory (may be slow for large projects)

Pytest Parameters

run_pytest_check exposes the following parameters for customization:

Note: Parallel test execution is enabled by default using pytest-xdist (-n auto).

Mypy Parameters

The mypy tools expose the following parameters for customization:

Command Line Interface (CLI)

Basic Usage

mcp-tools-py --project-dir /path/to/project [options]

Required Parameters

Optional Parameters

Python Configuration

Test Configuration

Logging Configuration

Notes

• When --venv-path is specified, it takes precedence over --python-executable

• The --console-only flag is useful during development to avoid creating log files

• Log files are created in JSON format for structured analysis

• Temporary files are automatically cleaned up unless --keep-temp-files is specified

Environment Configuration

The --python-executable and --venv-path options must point to the environment where pytest, pylint, and mypy are installed — this is typically the tool's own virtual environment, not your project's runtime venv.

Correct Configuration

Point to the venv where mcp-tools-py and its tools are installed:

{
    "mcpServers": {
        "mcp-tools-py": {
            "command": "mcp-tools-py",
            "args": [
                "--project-dir", "/path/to/your/project",
                "--venv-path", "${VIRTUAL_ENV}"
            ]
        }
    }
}

Incorrect Configuration

Do not point to your project's runtime venv if it doesn't have pytest/pylint/mypy installed:

{
    "mcpServers": {
        "mcp-tools-py": {
            "command": "mcp-tools-py",
            "args": [
                "--project-dir", "/path/to/your/project",
                "--venv-path", "/path/to/your/project/.venv"
            ]
        }
    }
}

This will fail if your project's .venv doesn't have the required tools installed.

Troubleshooting

• "No module named pytest" (or pylint/mypy): Your --python-executable or --venv-path points to an environment that doesn't have the required tools installed. Update the configuration to point to the correct environment.

• After installing missing tools, restart the MCP server for changes to take effect. Tool availability is checked at startup and cached for the session.

Installation

See INSTALL.md for detailed installation instructions.

Quick install:

# Install from GitHub (recommended)
pip install git+https://github.com/MarcusJellinghaus/mcp-tools-py.git

# Verify installation
mcp-tools-py --help

Development install:

# Clone and install for development
git clone https://github.com/MarcusJellinghaus/mcp-tools-py.git
cd mcp-tools-py
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
pip install -e ".[dev]"
mcp-tools-py --help

MCP Client Configuration

This server can be easily configured using the mcp-config Python tool. The mcp-config tool provides:

• Interactive setup: Works with Claude Desktop and VSCode

• Configuration management: Add, remove, and view server configurations

• Server repository: Access to curated MCP server collection

Prerequisites: Install Python and the mcp-config tool.

Note: While other MCP clients like Windsurf and Cursor support MCP servers, they may require manual configuration.

Using as a Dependency

In requirements.txt

Add this line to your requirements.txt:

mcp-tools-py @ git+https://github.com/MarcusJellinghaus/mcp-tools-py.git

In pyproject.toml

Add to your project dependencies:

[project]
dependencies = [
    "mcp-tools-py @ git+https://github.com/MarcusJellinghaus/mcp-tools-py.git",
    # ... other dependencies
]

# Or as an optional dependency
[project.optional-dependencies]
dev = [
    "mcp-tools-py @ git+https://github.com/MarcusJellinghaus/mcp-tools-py.git",
]

Installation Commands

After adding to requirements.txt or pyproject.toml:

# Install from requirements.txt
pip install -r requirements.txt

# Install from pyproject.toml
pip install .
# Or with optional dependencies
pip install ".[dev]"

Running the Server

Using the CLI Command (Recommended)

After installation, you can run the server using the mcp-tools-py command:

mcp-tools-py --project-dir /path/to/project [options]

Using Python Module (Alternative)

You can also run the server as a Python module:

python -m mcp_tools_py --project-dir /path/to/project [options]

# Or for development (from source directory)
python -m src.main --project-dir /path/to/project [options]

For detailed information about all available command-line options, see the CLI section.

Project Structure Support

The server automatically detects and analyzes Python code in standard project structures:

Default Analysis:

• src/ directory (if present) - Main source code

• tests/ directory (if present) - Test files

Custom Project Structures: Use the target_directories parameter to specify different directories:

# For a package-based structure
target_directories = ["mypackage", "tests"]

# For a simple project with code in root
target_directories = ["."]

# For complex multi-module projects
target_directories = ["module1", "module2", "shared", "tests"]

Structured Logging

The server provides comprehensive logging capabilities:

• Standard human-readable logs to console for development/debugging

• Structured JSON logs to file for analysis and monitoring

• Function call tracking with parameters, timing, and results

• Automatic error context capture with full stack traces

• Configurable log levels (DEBUG, INFO, WARNING, ERROR, CRITICAL)

• Default timestamped log files in project_dir/logs/mcp_tools_py_{timestamp}.log

Example structured log entries:

{
  "timestamp": "2025-08-05 14:30:15",
  "level": "info",
  "event": "Starting pylint check",
  "project_dir": "/path/to/project",
  "disable_codes": ["C0114", "C0116"],
  "target_directories": ["src", "tests"]
}

Use --console-only to disable file logging for simple development scenarios.

Quick MCP Client Setup

Automated Setup (Recommended)

1. First install the server:

   pip install git+https://github.com/MarcusJellinghaus/mcp-tools-py.git

2. Configure with mcp-config:

   mcp-config

   Then select "Add New" and search for this server, or run directly:

   mcp-config mcp-tools-py

This will prompt you for your project directory and automatically configure your MCP client.

Manual Setup

If you prefer manual configuration, edit your MCP configuration file:

Claude Desktop (%APPDATA%\Claude\claude_desktop_config.json on Windows):

{
    "mcpServers": {
        "mcp-tools-py": {
            "command": "mcp-tools-py",
            "args": ["--project-dir", "/path/to/your/project"]
        }
    }
}

For development mode:

{
    "mcpServers": {
        "mcp-tools-py": {
            "command": "python",
            "args": [
                "-m",
                "src.main",
                "--project-dir",
                "/path/to/your/project"
            ],
            "env": {
                "PYTHONPATH": "/path/to/mcp-tools-py"
            }
        }
    }
}

VSCode (.vscode/mcp.json):

{
    "servers": {
        "mcp-tools-py": {
            "command": "mcp-tools-py",
            "args": ["--project-dir", "."]
        }
    }
}

VSCode development mode:

{
    "servers": {
        "mcp-tools-py": {
            "command": "python",
            "args": ["-m", "src.main", "--project-dir", "."],
            "env": {
                "PYTHONPATH": "/path/to/mcp-tools-py"
            }
        }
    }
}

Testing with MCP Inspector

npx @modelcontextprotocol/inspector mcp-tools-py --project-dir /path/to/project

Available Tools

The server exposes the following MCP tools:

Run Pylint Check

• Runs pylint on the project code and generates smart prompts for LLMs

• Returns: A string containing either pylint results or a prompt for an LLM to interpret

• Helps identify code quality issues, style problems, and potential bugs

• Customizable with parameters for disabling specific pylint codes and targeting specific directories

• Supports flexible project structures through target_directories parameter

Run Pytest Check

• Runs pytest on the project code and generates smart prompts for LLMs

• Returns: A string containing either pytest results or a prompt for an LLM to interpret

• Identifies failing tests and provides detailed information about test failures

• Customizable with parameters for test selection, environment, and verbosity

Run Mypy Check

• Runs mypy type checking on the project code

• Returns: A string containing mypy results or a prompt for an LLM to interpret

• Identifies type errors and provides suggestions for better type safety

• Customizable with parameters for strict mode, error code filtering, and target directories

Development

Setting up the development environment

# Clone the repository
git clone https://github.com/MarcusJellinghaus/mcp-tools-py.git
cd mcp-tools-py

# Create and activate a virtual environment
python -m venv .venv
# On Windows:
.venv\Scripts\activate
# On Unix/MacOS:
source .venv/bin/activate

# Install dependencies
pip install -e .

# Install development dependencies
pip install -e ".[dev]"

Running with MCP Dev Tools

# Set the PYTHONPATH and run the server module using mcp dev
set PYTHONPATH=. && mcp dev src/server.py

License

This project is licensed under the MIT License - see the LICENSE file for details.

The MIT License is a permissive license that allows reuse with minimal restrictions. It permits use, copying, modification, and distribution with proper attribution.

Links

• MCP Python SDK

• MCP Filesystem Tools