Which integrations are available for this server?

Runs [pytest](/mcp/servers/integrations/pytest) tests on project code, identifies failing tests, and generates smart prompts for LLMs to explain issues and suggest fixes Performs comprehensive code quality checks including pylint analysis, mypy type checking, and pytest execution on [Python](/mcp/servers/integrations/python) projects with configurable parameters for each tool

MCP Code Checker

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 systems to perform quality checks on your code. With these capabilities, AI assistants can:

Run pylint checks to identify code quality issues
Execute pytest to identify failing tests
Run mypy for type checking
Generate smart prompts for LLMs to explain issues and suggest fixes
Combine multiple checks for comprehensive code quality analysis

All operations are securely contained within your specified project directory, giving you control while enabling powerful AI collaboration for code quality improvement.

By connecting your AI assistant to your code checking tools, you can transform your debugging workflow - describe what you need in natural language and let the AI identify and fix issues directly in your project files.

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
run_all_checks: Run all code checks (pylint, pytest, and mypy) and generate combined results

Pylint Parameters

The pylint tools expose the following parameters for customization:

Parameter	Type	Default	Description
`categories`	list	['error', 'fatal']	List of pylint message categories to include
`disable_codes`	list	None	List of pylint error codes to disable during analysis
`target_directories`	list	["src", "tests"]	List of directories to analyze relative to project_dir

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

Both run_pytest_check and run_all_checks expose the following parameters for customization:

Parameter	Type	Default	Description
`markers`	list	None	Optional list of pytest markers to filter tests
`verbosity`	integer	2	Pytest verbosity level (0-3)
`extra_args`	list	None	Optional list of additional pytest arguments
`env_vars`	dictionary	None	Optional environment variables for the subprocess

Mypy Parameters

The mypy tools expose the following parameters for customization:

Parameter	Type	Default	Description
`strict`	boolean	True	Use strict mode settings
`disable_error_codes`	list	None	List of mypy error codes to ignore
`target_directories`	list	["src", "tests"]	List of directories to check relative to project_dir
`follow_imports`	string	'normal'	How to handle imports during type checking

Command Line Interface (CLI)

Basic Usage

mcp-code-checker --project-dir /path/to/project [options]

Required Parameters

Parameter	Type	Description
`--project-dir`	string	Required. Base directory for code checking operations

Optional Parameters

Python Configuration

Parameter	Type	Default	Description
`--python-executable`	string	sys.executable	Path to Python interpreter to use for running tests
`--venv-path`	string	None	Path to virtual environment to activate. When specified, this venv's Python will be used instead of `--python-executable`

Test Configuration

Parameter	Type	Default	Description
`--test-folder`	string	"tests"	Path to the test folder (relative to project-dir)
`--keep-temp-files`	flag	False	Keep temporary files after test execution. Useful for debugging when tests fail

Logging Configuration

Parameter	Type	Default	Description
`--log-level`	string	"INFO"	Set logging level. Choices: DEBUG, INFO, WARNING, ERROR, CRITICAL
`--log-file`	string	None	Path for structured JSON logs. If not specified, logs only to console
`--console-only`	flag	False	Log only to console, ignore `--log-file` parameter

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

Installation

See INSTALL.md for detailed installation instructions.

Quick install:

# Install from GitHub (recommended) pip install git+https://github.com/MarcusJellinghaus/mcp-code-checker.git # Verify installation mcp-code-checker --help

Development install:

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

In pyproject.toml

Add to your project dependencies:

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

mcp-code-checker --project-dir /path/to/project [options]

Using Python Module (Alternative)

You can also run the server as a Python module:

python -m mcp_code_checker --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_code_checker_{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)

First install the server:
pip install git+https://github.com/MarcusJellinghaus/mcp-code-checker.git
Configure with mcp-config:
mcp-config
Then select "Add New" and search for this server, or run directly:
mcp-config mcp-code-checker

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": { "code_checker": { "command": "mcp-code-checker", "args": ["--project-dir", "/path/to/your/project"] } } }

For development mode:

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

VSCode (.vscode/mcp.json):

{ "servers": { "code-checker": { "command": "mcp-code-checker", "args": ["--project-dir", "."] } } }

VSCode development mode:

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

Testing with MCP Inspector

npx @modelcontextprotocol/inspector mcp-code-checker --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

Run All Checks

Runs all code checks (pylint, pytest, and mypy) and generates combined results
Returns: A string containing results from all checks and/or LLM prompts
Provides a comprehensive analysis of code quality in a single operation
Supports customization parameters for all three tools, including target directories

Security Features

All checks are performed within the specified project directory
Code execution is limited to the Python test files within the project
Results are formatted for easy interpretation by both humans and LLMs
Directory traversal protection through validation of target directories

Development

Setting up the development environment

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