MCP Code Reviewer
Click on "Install 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: EX MCP Server
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
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