Ohm MCP
An AI-driven Python code refactoring and quality analysis assistant that provides functions such as code refactoring, architecture optimization, and performance detection through AST analysis, and is compatible with the MCP protocol of multiple IDEs.
2.5 points
6.6K

What is OHM-MCP?

OHM-MCP is an intelligent code refactoring server based on the Model Context Protocol, specifically designed to provide AI-driven quality analysis and automated refactoring functions for Python code. It can understand your code structure, detect quality issues, and safely apply refactoring improvements.

How to use OHM-MCP?

OHM-MCP integrates with your AI assistants (such as GitHub Copilot, Cursor IDE, etc.) through the MCP protocol. After installation, you can directly use the #ohm-mcp command in the AI chat to analyze and refactor code without leaving the development environment.

Applicable scenarios

Suitable for Python project maintenance, code quality improvement, technical debt management, unifying team code specifications, and safely conducting large-scale refactoring. Particularly suitable for handling legacy code, increasing test coverage, and optimizing performance bottlenecks.

Main Features

Intelligent Code Analysis
100% accurate code analysis based on AST (Abstract Syntax Tree) to detect dead code, duplicate code, architecture issues, etc.
Safe Automated Refactoring
Automatically extract methods, rename symbols, refactor imports, etc. Automatically back up before each operation and automatically roll back in case of failure.
Type Safety Enhancement
Analyze type hint coverage, generate type stub files, and support progressive type migration.
Performance Optimization Detection
Identify performance issues such as O(n²) complexity patterns, nested loops, and mutable default parameters.
Automatic Test Generation
Automatically generate feature tests for existing code to ensure consistent behavior before and after refactoring.
Design Pattern Suggestion
Detect design problems in the code and recommend appropriate refactoring patterns and design patterns.
Quality Dashboard
Generate code quality reports in HTML/JSON/Markdown format to visually track the improvement progress.
Multi-IDE Compatibility
Supports all MCP-compatible AI assistants such as GitHub Copilot, Cursor IDE, and Cline.
Advantages
100% AST-driven, more accurate and reliable than traditional regular expression methods
Fully automated, from analysis to applying refactoring in one step
Built-in security mechanism, automatic backup and rollback ensure code safety
Seamlessly integrated with the development environment, no need to switch tools
Supports progressive improvement, can start from a small scope and gradually promote
Provides visual quality reports for easy team progress tracking
Limitations
Currently only supports the Python language
Requires AI assistants to support the MCP protocol
Complex refactoring may require manual review
The first analysis of large projects may take a long time

How to Use

Choose an Installation Method
It is recommended to use the NPX method for automatic installation, or use the PyPI method for manual installation.
Configure the AI Assistant
Add MCP server configuration according to the AI assistant you use (GitHub Copilot, Cursor IDE, etc.).
Use in Chat
In the AI assistant chat window, use the #ohm-mcp command prefix to call various tools.
View Results and Apply
View the analysis results and selectively apply the recommended refactoring. The system will automatically back up and verify.

Usage Examples

Clean Up Unused Code
When maintaining large projects, there are often unused imports and functions. Manual cleaning is time-consuming and error-prone.
Safely Refactor Complex Functions
A function is too complex and needs to be split into multiple small functions to improve readability and maintainability.
Improve Type Safety
The project needs to migrate from no type hints to full typing, but you don't know where to start.
Team Code Quality Tracking
The team needs to regularly evaluate code quality and track the improvement progress.
Unify Naming Conventions
There are inconsistent function names in the project and need to be renamed uniformly.

Frequently Asked Questions

Will OHM-MCP affect my existing code?
Do I need to install Python?
Which AI assistants are supported?
How long does it take to analyze a large project?
How to ensure the safety of refactoring?
Can I customize the analysis rules?
Does it support other programming languages?
How to report issues or suggest features?

Related Resources

Official GitHub Repository
Source code, issue tracking, and contribution guidelines
MCP Protocol Official Website
Official documentation and specifications of the Model Context Protocol
PyPI Package Page
OHM-MCP page on the Python Package Index
MCP Registry
Official MCP server registry entry
Example Configuration Repository
Configuration examples and use case demonstrations for various IDEs

Installation

Copy the following command to your Client for configuration
{
  "mcpServers": {
    "ohm-mcp": {
      "command": "npx",
      "args": ["ohm-mcp@latest"]
    }
  }
}

{
  "mcpServers": {
    "ohm-mcp": {
      "command": "npx",
      "args": ["--package", "/path/to/ohm-mcp-npm", "ohm-mcp"]
    }
  }
}

{
  "mcpServers": {
    "ohm-mcp": {
      "command": "npx",
      "args": ["-y", "ohm-mcp@latest"]
    }
  }
}

{
  "mcpServers": {
    "ohm-mcp": {
      "command": "npx",
      "args": ["-y", "ohm-mcp@latest"],
      "env": {
        "PYTHONUNBUFFERED": "1"
      }
    }
  }
}

{
  "mcpServers": {
    "ohm-mcp": {
      "command": "/Users/username/project/venv/bin/python",
      "args": ["-m", "ohm_mcp.server"],
      "disabled": false,
      "alwaysAllow": []
    }
  }
}

{
  "mcpServers": {
    "ohm-mcp": {
      "command": "python",
      "args": ["-m", "ohm_mcp.server"]
    }
  },
  "inputs": []
}

{
  "mcpServers": {
    "ohm-mcp": {
      "command": "/Users/username/projects/venv/bin/python",
      "args": ["-m", "ohm_mcp.server"]
    }
  }
}

{
  "mcpServers": {
    "ohm-mcp": {
      "command": "python",
      "args": ["-m", "ohm_mcp.server"]
    }
  }
}

{
  "mcpServers": {
    "ohm-mcp": {
      "command": "<python-interpreter-path>",
      "args": ["<path-to-mcp_server.py>"],
      "cwd": "<project-directory>"
    }
  }
}
Note: Your key is sensitive information, do not share it with anyone.

Alternatives

V
Vestige
Vestige is an AI memory engine based on cognitive science. By implementing 29 neuroscience modules such as prediction error gating, FSRS - 6 spaced repetition, and memory dreaming, it provides long - term memory capabilities for AI. It includes a 3D visualization dashboard and 21 MCP tools, runs completely locally, and does not require the cloud.
Rust
10.5K
4.5 points
M
Moltbrain
MoltBrain is a long-term memory layer plugin designed for OpenClaw, MoltBook, and Claude Code, capable of automatically learning and recalling project context, providing intelligent search, observation recording, analysis statistics, and persistent storage functions.
TypeScript
10.1K
4.5 points
B
Bm.md
A feature-rich Markdown typesetting tool that supports multiple style themes and platform adaptation, providing real-time editing preview, image export, and API integration capabilities
TypeScript
15.9K
5 points
S
Security Detections MCP
Security Detections MCP is a server based on the Model Context Protocol that allows LLMs to query a unified security detection rule database covering Sigma, Splunk ESCU, Elastic, and KQL formats. The latest version 3.0 is upgraded to an autonomous detection engineering platform that can automatically extract TTPs from threat intelligence, analyze coverage gaps, generate SIEM-native format detection rules, run tests, and verify. The project includes over 71 tools, 11 pre-built workflow prompts, and a knowledge graph system, supporting multiple SIEM platforms.
TypeScript
6.7K
4 points
P
Paperbanana
Python
8.9K
5 points
B
Better Icons
An MCP server and CLI tool that provides search and retrieval of over 200,000 icons, supports more than 150 icon libraries, and helps AI assistants and developers quickly obtain and use icons.
TypeScript
8.7K
4.5 points
A
Assistant Ui
assistant - ui is an open - source TypeScript/React library for quickly building production - grade AI chat interfaces, providing composable UI components, streaming responses, accessibility, etc., and supporting multiple AI backends and models.
TypeScript
10.0K
5 points
A
Apify MCP Server
The Apify MCP Server is a tool based on the Model Context Protocol (MCP) that allows AI assistants to extract data from websites such as social media, search engines, and e-commerce through thousands of ready-to-use crawlers, scrapers, and automation tools (Apify Actors). It supports OAuth and Skyfire proxy payment and can be integrated into MCP clients such as Claude and VS Code through HTTPS endpoints or local stdio.
TypeScript
8.9K
5 points
M
Markdownify MCP
Markdownify is a multi-functional file conversion service that supports converting multiple formats such as PDFs, images, audio, and web page content into Markdown format.
TypeScript
38.1K
5 points
D
Duckduckgo MCP Server
Certified
The DuckDuckGo Search MCP Server provides web search and content scraping services for LLMs such as Claude.
Python
80.3K
4.3 points
G
Gitlab MCP Server
Certified
The GitLab MCP server is a project based on the Model Context Protocol that provides a comprehensive toolset for interacting with GitLab accounts, including code review, merge request management, CI/CD configuration, and other functions.
TypeScript
28.5K
4.3 points
N
Notion Api MCP
Certified
A Python-based MCP Server that provides advanced to-do list management and content organization functions through the Notion API, enabling seamless integration between AI models and Notion.
Python
23.8K
4.5 points
U
Unity
Certified
UnityMCP is a Unity editor plugin that implements the Model Context Protocol (MCP), providing seamless integration between Unity and AI assistants, including real - time state monitoring, remote command execution, and log functions.
C#
37.4K
5 points
F
Figma Context MCP
Framelink Figma MCP Server is a server that provides access to Figma design data for AI programming tools (such as Cursor). By simplifying the Figma API response, it helps AI more accurately achieve one - click conversion from design to code.
TypeScript
70.7K
4.5 points
G
Gmail MCP Server
A Gmail automatic authentication MCP server designed for Claude Desktop, supporting Gmail management through natural language interaction, including complete functions such as sending emails, label management, and batch operations.
TypeScript
24.0K
4.5 points
C
Context7
Context7 MCP is a service that provides real-time, version-specific documentation and code examples for AI programming assistants. It is directly integrated into prompts through the Model Context Protocol to solve the problem of LLMs using outdated information.
TypeScript
106.2K
4.7 points
AIBase
Zhiqi Future, Your AI Solution Think Tank
© 2026AIBase