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 LLMsrun_pytest_check: Run pytest on the project code and generate smart prompts for LLMsrun_mypy_check: Run mypy type checking on the project coderun_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-pathis specified, it takes precedence over--python-executable - The
--console-onlyflag 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-filesis 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 codetests/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-configThen 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_directoriesparameter
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.