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MCP Code Analyzer

by emiryasar

The MCP Code Analyzer intelligently adapts changes to a project by updating related usages when code modifications are made. It integrates with Claude Desktop to analyze code and provide insights for changes.

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What is MCP Code Analyzer?

The Model Context Protocol (MCP) Code Analyzer is a comprehensive code analysis and management tool that integrates with Claude Desktop. It analyzes code at both project and file levels, providing insights for needed changes and project understanding.

How to use MCP Code Analyzer?

First, clone the repository and install the dependencies using pip. Then, configure Claude Desktop by editing the claude_desktop_config.json file to include the MCP server configuration, specifying the command and arguments to run the analyzer. You can then analyze project paths and files through chat messages.

Key features of MCP Code Analyzer

  • Project Analysis (structure, statistics, technology)

  • Code Analysis (imports, file analysis, references)

  • File Operations (with version control)

  • Code Modification (line by line)

Use cases of MCP Code Analyzer

  • Automated code refactoring

  • Project understanding and analysis

  • Finding code references

  • Automated code documentation

FAQ from MCP Code Analyzer

What is the purpose of the MCP Code Analyzer?

The MCP tool aims to adapt changes to the project intelligently, ensuring that related usages are updated accordingly when a code modification or structural adjustment is needed.

What are the prerequisites for using the MCP Code Analyzer?

You need Python 3.10 or later and a Windows environment. Linux/MacOS support is not yet tested.

How do I install the MCP Code Analyzer?

Clone the repository using git and install the dependencies using pip install -e .

How do I integrate the MCP Code Analyzer with Claude Desktop?

Access Claude Desktop config, create/edit claude_desktop_config.json and configure the mcpServers section.

What are the known issues with the MCP Code Analyzer?

Chat context limitations may interrupt large file modifications, and AI-generated documentation comments can affect code operation in large files.