MCP Code Reviewer
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP Code Revieweranalyze this Python code: def foo(): print('hello')"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
๐ค MCP Code Reviewer Demo
This project demonstrates Model Context Protocol (MCP) with an AI-powered Code Reviewer.
Features
analyze_code: Finds basic issues in Python codesuggest_refactor: Suggests improvements (e.g., replace print with logging)write_tests: Auto-generates placeholder unit testsAgentic Mode: Automatically analyzes โ refactors โ re-analyzes code until clean
Related MCP server: Argus MCP
Quick Start
pip install -r requirements.txt
python -m mcp_code_reviewer.mcp_server # start server
python -m mcp_code_reviewer.mcp_client # run demo client๐ Using Makefile
For convenience, a Makefile is provided:
make install # install dependencies
make server # run MCP server
make client # run demo client
make client-agent # run demo client in agentic loop mode
make test # run tests
make clean # remove caches and logs๐ Demo Output
Standard Demo (make client)
Available tools: ['analyze_code', 'suggest_refactor', 'write_tests']
๐ Analysis:
{
"issues": ["Consider using logging instead of print statements."],
"line_count": 2
}
๐ Refactor Suggestion:
{
"original": "def foo():\n print('Hello')",
"refactored": "def foo():\n logger.info('Hello')"
}
๐งช Generated Tests:
{
"tests": "def test_placeholder():\n assert True"
}๐ค Agentic Mode Demo (make client-agent)
๐ Iteration 1: Analyzing code...
Analysis: {
"issues": ["Consider using logging instead of print statements."],
"line_count": 2
}
โ ๏ธ Issues found, applying refactor...
๐ Iteration 2: Analyzing code...
Analysis: {
"issues": [],
"line_count": 2
}
โ
No issues found! Code is clean.
Final Code:
def foo():
logger.info('Hello')Why This Project?
Showcases MCP server + client implementation
Demonstrates GenAI-style tooling (review, refactor, tests)
Adds Agentic AI loop to show self-improving code refinement
Strong example of MCP + GenAI + automation for recruiters
Next Steps
Integrate with real LLMs for deeper code analysis
Expand test coverage & CI integration
Record an asciinema demo and embed it here for a live showcase
This server cannot be deployed
Maintenance
Related MCP Connectors
Code intelligence platform for AI agents. 20 tools for architecture, security & impact analysis.
Deterministic AI code review, with an audit record. Governance inside the agent loop.
Agentic code review, no signup to try: reality gates + frontier-model review, with veto.
AI-powered codebase analysis โ call graphs, security, dead code, complexity. 150+ tools.
Related MCP Servers
- AlicenseAqualityDmaintenanceEnables comprehensive code analysis including quality assessment, security vulnerability detection, refactoring suggestions, complexity calculations, and automatic documentation generation for multiple programming languages.57 npmMIT
- AlicenseNot gradedqualityCmaintenanceEnables AI-powered, zero-trust code review with multiple models, supporting single files, git diffs, and multiple files, with security, performance, and architecture checks across 10+ languages.13MIT

MatterAI MCP Serverofficial
AlicenseNot gradedqualityCmaintenanceEnables code reviews, implementation planning, and pull request generation for AI agents in IDEs like Cursor and Windsurf.1MIT
CodePeel MCP Serverofficial
FlicenseAqualityDmaintenanceEnables AI agents to review code diffs for bugs, security issues, and bad patterns, and generate fixes.4-