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MCP Code Checker: AI-Driven Code Reviews & Instant Fixes - MCP Implementation

MCP Code Checker: AI-Driven Code Reviews & Instant Fixes

MCP Code Checker automates pylint/pytest analysis with AI-friendly prompts, letting Claude & other LLMs swiftly suggest fixes. Smarter code reviews, faster fixes—no guesswork.

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Users create an average of 36 projects per month with this tool

About MCP Code Checker

What is MCP Code Checker: AI-Driven Code Reviews & Instant Fixes?

MCP Code Checker is an AI-powered code analysis tool designed to streamline code quality assurance. Built on the Model Context Protocol (MCP), it enables AI assistants like Claude (via Claude Desktop) to perform automated code reviews, execute test suites, and generate actionable insights. This server acts as a bridge between your codebase and AI capabilities, offering real-time feedback and instant fixes while maintaining strict directory-level security.

How to Use MCP Code Checker: AI-Driven Code Reviews & Instant Fixes?

Implementing MCP Code Checker involves three core steps: installation, server configuration, and AI integration. Begin by cloning the repository and setting up a virtual environment. Launch the server with your project directory specified, then configure AI platforms like Claude Desktop to communicate with the MCP endpoint. Advanced users can leverage tools like MCP Inspector for debugging server operations.

MCP Code Checker Features

Key Features of MCP Code Checker: AI-Driven Code Reviews & Instant Fixes?

  • Automated Static Analysis: Runs pylint to detect style violations and potential bugs
  • Test Execution: Integrates pytest to identify failing tests and coverage gaps
  • LLM-Powered Insights: Generates context-rich prompts for AI assistants to suggest fixes
  • Comprehensive Reports: Combines static analysis and test results into unified reports
  • Secure Sandboxing: Restricts all operations to user-defined project directories

Use Cases of MCP Code Checker: AI-Driven Code Reviews & Instant Fixes?

Common applications include:

  • Automating pre-commit code reviews in CI/CD pipelines
  • Real-time debugging assistance during development
  • Onboarding new developers with instant code quality feedback
  • Security audits using AI-generated risk assessment prompts
  • Legacy code modernization through targeted improvement suggestions

MCP Code Checker FAQ

FAQ from MCP Code Checker: AI-Driven Code Reviews & Instant Fixes?

How is security maintained?
All operations are confined to specified project directories, with execution limited to test files only. MCP's sandboxed architecture prevents unintended system access.

What AI systems are compatible?
Works natively with Claude Desktop and any MCP-compliant AI platform. LLM compatibility depends on prompt generation capabilities.

Can I customize checks?
While current tools focus on pylint and pytest, developers can extend functionality using the MCP protocol specifications.

Where are logs stored?
Claude Desktop logs reside at %APPDATA%\Claude\logs (Windows) or ~/Library/Application Support/Claude/logs (macOS).

Need troubleshooting help?
Visit the MCP Documentation or use MCP Inspector's debugging interface for server diagnostics.

Content

MCP Code Checker

A Model Context Protocol (MCP) server providing code quality checking operations. This server offers a 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) 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
  • 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_all_checks: Run all code checks (pylint and pytest) and generate combined results

Installation

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

# Create and activate a virtual environment (optional but recommended)
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install dependencies
pip install -e .

Running the Server

python -m src.main --project-dir /path/to/project

The server uses FastMCP for operation. The project directory parameter (--project-dir) is required for security reasons. All code checking operations will be restricted to this directory.

Using with Claude Desktop App

To enable Claude to use this code checking server for analyzing files in your local environment:

  1. Create or modify the Claude configuration file:
* Location: `%APPDATA%\Claude\claude_desktop_config.json` (on Windows)
* On macOS: `~/Library/Application Support/Claude/claude_desktop_config.json`
  1. Add the MCP server configuration to the file:
{
    "mcpServers": {
        "code_checker": {
            "command": "C:\\path\\to\\mcp_code_checker\\.venv\\Scripts\\python.exe",
            "args": [                
                "C:\\path\\to\\mcp_code_checker\\src\\main.py",
                "--project-dir",
                "C:\\path\\to\\your\\project"
            ],
            "env": {
                "PYTHONPATH": "C:\\path\\to\\mcp_code_checker\\"
            }
        }
    }
}
  1. Replace all C:\\path\\to\\ instances with your actual paths:
* Point to your Python virtual environment
* Set the project directory to the folder you want Claude to check
* Make sure the PYTHONPATH points to the mcp_code_checker root folder
  1. Restart the Claude desktop app to apply changes

Claude will now be able to analyze code in your specified project directory.

  1. Log files location:
    * Windows: %APPDATA%\Claude\logs
    * These logs can be helpful for troubleshooting issues with the MCP server connection

For more information on logging and troubleshooting, see the MCP Documentation.

Using MCP Inspector

MCP Inspector allows you to debug and test your MCP server:

  1. Start MCP Inspector by running:
npx @modelcontextprotocol/inspector \
  uv \
  --directory C:\path\to\mcp_code_checker \
  run \
  src\main.py
  1. In the MCP Inspector web UI, configure with the following:
* Python interpreter: `C:\path\to\mcp_code_checker\.venv\Scripts\python.exe`
* Arguments: `C:\path\to\mcp_code_checker\src\main.py --project-dir C:\path\to\your\project`
* Environment variables: 
  * Name: `PYTHONPATH`
  * Value: `C:\path\to\mcp_code_checker\`
  1. This will launch the server and provide a debug interface for testing the available tools.

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

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

Run All Checks

  • Runs all code checks (pylint and pytest) 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

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

Development

Setting Up Development Environment

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

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

# Install dependencies
pip install -e .

# For 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

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