🧠 MCP Code Assistant

🧠 MCP-Based AI Code Analysis Tool

An AI-powered developer tool built on the Model Context Protocol (MCP) that enables LLM agents to interact with, analyze, and debug codebases via structured MCP tool calls — scanning project structure, extracting code elements, and detecting Python anti-patterns.

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🌟 Overview

MCP-Based AI Code Analysis Tool bridges the gap between LLM agents and real codebases. It exposes project structure, file contents, class/function maps, and bug detection as structured MCP-compatible tool calls — allowing AI agents (Claude, GPT-4, etc.) to reason about and navigate codebases without manual context injection.

Key capabilities:

• 🔗 MCP Tool Integration: Exposes codebase operations as structured tools consumable by LLM agents
• 🔍 Project Scanning: Maps your entire project structure and identifies all code files
• 🧠 Code Analysis: Extracts classes, functions, imports, and global variables from Python files
• 🔎 Pattern Searching: Searches your entire codebase for specific patterns using regex
• 🐛 Bug Detection: Identifies common Python bugs and anti-patterns
• 📁 File Management: Updates file content programmatically

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📋 Requirements

Python 3.6 or higher — No external libraries required (uses only Python standard library)

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🔧 Installation

git clone https://github.com/kiranimmadi2/MCP-Server.git
cd MCP-Server

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📚 Usage

Command Line Interface

python mcp.py /path/to/your/project [options]

Option	Description
--scan	Scan the project directory
--structure	Print the project structure
--analyze <file>	Analyze a specific Python file
--search <regex>	Search for a regex pattern in code
--bugs	Find potential bugs in Python files

Examples

# Scan and show structure
python mcp.py /path/to/project --scan --structure

# Analyze a file
python mcp.py /path/to/project --analyze path/to/file.py

# Find bugs
python mcp.py /path/to/project --bugs

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🧩 Using as a Python Library / MCP Tool Backend

from mcp import MCP

mcp = MCP("/path/to/your/project")
mcp.scan_project()
mcp.print_structure()

# Structured output for LLM consumption
analysis = mcp.analyze_python_file("path/to/file.py")
results = mcp.search_code(r"class\s+[A-Z][a-zA-Z0-9_]*")
bugs = mcp.find_bugs()

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🛣️ Roadmap

• Full MCP server mode with JSON-RPC tool registration
• LLM agent integration examples (Claude + LangChain)
• Support for JavaScript and TypeScript
• Code complexity scoring

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📄 License

MIT License — see the LICENSE file for details.

Built with pure Python. Designed for the AI-native developer workflow.