Ai Develop Assistant

Name: Ai Develop Assistant
Author: jieshao11

@jieshao11

Repository

About Ai Develop Assistant

协助AI开发者需求完善、模块设计、技术架构设计的mcp

#ai-develop-assistant #mcp #project-management

Basic information

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "ai-develop-assistant": {
      "command": "uvx",
      "args": [
        "ai-develop-assistant@latest"
      ],
      "env": {
        "MCP_STORAGE_DIR": "/path/to/your/storage"
      }
    }
  }
}

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Ai Develop Assistant?

Ai Develop Assistant is an MCP server that helps developers clarify requirements, design architecture, and export project documentation. It integrates with the Claude Desktop environment and stores data persistently in local files.

How to use Ai Develop Assistant?

Configure the server in the Claude Desktop JSON config file, set the MCP_STORAGE_DIR environment variable, and restart Claude Desktop. Then use its five tools (requirement_clarifier, requirement_manager, architecture_designer, export_final_document, view_requirements_status) to follow a complete project analysis flow.

Key features of Ai Develop Assistant

Requirement clarification and management tools
Architecture design generation tool
Persistent storage with automatic saves
Multi-format export (JSON + Markdown)
Complete operation history tracking
Configurable storage directory via environment variable

Use cases of Ai Develop Assistant

Clarifying vague project requirements with structured prompts
Managing requirement documents with version history
Generating architecture designs for web applications
Exporting final project documentation in JSON and Markdown
Tracking analysis progress with status viewing tool

FAQ from Ai Develop Assistant

What kind of projects can Ai Develop Assistant handle?

The README demonstrates a Web AI resource sharing site example, suggesting it handles general software project analysis and architecture design.

What are the runtime requirements?

The server runs with Python and is configured via the Claude Desktop claude_desktop_config.json file. A test script (test_optimized_mcp.py) is provided for validation.

Where does the data live?

All data is stored locally in the directory specified by the MCP_STORAGE_DIR environment variable (default ./mcp_data). Files include requirements.json, history.json, and exported documents.

How does it compare to the original version?

Improvements over the original include data persistence, history tracking, document export, real-time status viewing, and flexible storage configuration.