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MCP 精细代码分析

一种MCP服务器,它使大语言模型(LLMs)能够通过函数调用图来理解和分析代码结构,从而使AI助手能够探索函数之间的关系并分析Python代码库中的依赖关系。

article

README

Nuanced MCP Server

A Model Context Protocol (MCP) server that provides call graph analysis capabilities to LLMs through the nuanced library.

Overview

This MCP server enables LLMs to understand code structure by accessing function call graphs through standardized tools and resources. It allows AI assistants to:

  • Initialize call graphs for Python repos
  • Explore function call relationships
  • Analyze dependencies between functions
  • Provide more contextually aware code assistance

API

Tools

  • initialize_graph

    • Initialize a code graph for the given repository path
    • Input: repo_path (string)
  • switch_repository

    • Switch to a different initialized repository
    • Input: repo_path (string)
  • list_repositories

    • List all initialized repositories
    • No inputs required
  • get_function_call_graph

    • Get the call graph for a specific function
    • Inputs:
      • file_path (string)
      • function_name (string)
      • repo_path (string, optional) - uses active repository if not specified
  • analyze_dependencies

    • Find all module or file dependencies in the codebase
    • Inputs (at least one required):
      • file_path (string, optional)
      • module_name (string, optional)
  • analyze_change_impact

    • Analyze the impact of changing a specific function
    • Inputs:
      • file_path (string)
      • function_name (string)

Resources

  • graph://summary

    • Get a summary of the currently loaded code graph
    • No parameters required
  • graph://repo/{repo_path}/summary

    • Get a summary of a specific repository's code graph
    • Parameters:
      • repo_path (string) - Path to the repository
  • graph://function/{file_path}/{function_name}

    • Get detailed information about a specific function
    • Parameters:
      • file_path (string) - Path to the file containing the function
      • function_name (string) - Name of the function to analyze

Prompts

  • analyze_function

    • Create a prompt to analyze a function with its call graph
    • Parameters:
      • file_path (string) - Path to the file containing the function
      • function_name (string) - Name of the function to analyze
  • impact_analysis

    • Create a prompt to analyze the impact of changing a function
    • Parameters:
      • file_path (string) - Path to the file containing the function
      • function_name (string) - Name of the function to analyze
  • analyze_dependencies_prompt

    • Create a prompt to analyze dependencies of a file or module
    • Parameters (at least one required):
      • file_path (string, optional) - Path to the file to analyze
      • module_name (string, optional) - Name of the module to analyze

Usage with Claude Desktop

Add this to your claude_desktop_config.json

UV

{
  "mcpServers": {
    "nuanced": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/nuanced-mcp",
        "run",
        "nuanced_mcp_server.py"
      ]
    }
  }
}
help

运行方式说明

cloud

托管运行

托管运行通常表示这个 MCP Server 由服务方环境承载,用户一般按页面提供的连接方式或授权流程接入,不需要在本地长期启动一个 MCP 进程

  1. 打开服务方连接页
  2. 完成授权或复制端点
  3. 在 MCP 客户端中连接
terminal

本地运行 / 其它方式

本地运行通常需要用户在自己的电脑或服务器上安装依赖,把 server_config 复制到 MCP 客户端,并按 env_schema 补齐环境变量、密钥或其它配置

  1. 复制 server_config
  2. 安装所需依赖
  3. 补齐环境变量后重启客户端