product-spec-mcp

防止 AI 编程一上来就乱写代码的 MCP 工具。

把用户的模糊产品想法变成追问清单、可执行规格、架构建议和验收标准。

适用场景

当你或 AI 编程助手拿到一句话需求（"做一个报名系统"、"页面高级一点"、"接口报错了"），不要直接开工。先过一遍需求闸门，减少返工。

Related MCP server: DinCoder

推荐流程

1. spec_interrogate   → 评估需求完整度，生成追问清单
2. spec_compile       → 编译产品规格和开发 Prompt
3. architecture_decide → 判断架构方案
4. acceptance_generate → 生成验收标准

不确定用哪个工具？ 先用 product_spec_assist，它会自动识别场景并调用合适的工具。

Features

This MCP Server provides 7 tools for product development workflow:

Installation

npm install
npm run build

Usage

As MCP Server (stdio)

npm start

Development Mode

npm run dev

MCP Client Configuration

Claude Desktop

Add to your Claude Desktop configuration (claude_desktop_config.json):

{
  "mcpServers": {
    "product-spec": {
      "command": "node",
      "args": ["/path/to/product-spec-mcp/dist/index.cjs"]
    }
  }
}

Cursor

Add to your Cursor MCP configuration (.cursor/mcp.json):

{
  "mcpServers": {
    "product-spec": {
      "command": "node",
      "args": ["/path/to/product-spec-mcp/dist/index.cjs"]
    }
  }
}

VS Code (Continue)

Add to your Continue configuration:

{
  "mcpServers": {
    "product-spec": {
      "command": "node",
      "args": ["/path/to/product-spec-mcp/dist/index.cjs"]
    }
  }
}

Tools Documentation

product_spec_assist (推荐入口)

统一入口：根据用户原话自动判断场景并调用对应能力。

Input:

• message (required): 用户原话

• known_context: 已有上下文

• preferred_platform: web | mini_program | app | backend | unknown

• strictness: light | normal | grill

• auto_execute: boolean (default: true)

Example:

{
  "message": "我想做一个报名系统，学生可以提交资料，后台老师审核",
  "preferred_platform": "web"
}

路由规则:

spec_interrogate

Analyze requirement completeness and generate clarification questions.

Input:

• raw_idea (required): User's original idea description

• scenario: build_product | modify_ui | debug | launch | unknown

• target_platform: web | mini_program | app | backend | unknown

• strictness: light | normal | grill

• known_context: Object with known context information

Example:

{
  "raw_idea": "我想做一个报名系统，用户可以提交资料，后台能看到",
  "scenario": "build_product",
  "target_platform": "web"
}

spec_compile

Compile full product specification and development prompt.

Input:

• raw_idea (required): User's original idea

• answers: Object with answers to clarification questions

• allow_assumptions: boolean (default: true)

• min_readiness_score: number (default: 70)

Example:

{
  "raw_idea": "报名系统",
  "answers": {
    "target_user": "学生",
    "platform": "web",
    "data_persistence": true
  },
  "allow_assumptions": true
}

architecture_decide

Make architecture decisions based on product type and features.

Input:

• product_type (required): Product type description

• platform (required): web | mini_program | app | backend

• features (required): Array of feature descriptions

• commercial_intent: boolean

• expected_users: individual | small_team | enterprise | massive

Example:

{
  "product_type": "电商系统",
  "platform": "web",
  "features": ["商品展示", "购物车", "支付", "订单管理"],
  "commercial_intent": true,
  "expected_users": "small_team"
}

ui_translate

Translate user UI descriptions into frontend terminology.

Input:

• description (required): User's UI description

• current_page: Current page name

• target_component: Target component name

Example:

{
  "description": "首页看起来太廉价了，高级一点",
  "current_page": "首页"
}

debug_guide

Generate structured debugging checklists.

Input:

• platform (required): web | mini_program | app | backend | build | unknown

• error_description (required): Error description

• current_info: Object with known error information

Example:

{
  "platform": "web",
  "error_description": "点击提交按钮后页面白屏"
}

acceptance_generate

Generate acceptance criteria for features.

Input:

• product_type (required): Product type

• features (required): Array of features

• platform (required): web | mini_program | app | backend

• has_backend: boolean

• has_payment: boolean

• has_auth: boolean

Example:

{
  "product_type": "表单工具",
  "features": ["表单提交", "数据查看"],
  "platform": "web",
  "has_backend": true
}

Development

Run Tests

npm test

Type Check

npm run typecheck

Build

npm run build

Architecture

src/
├── index.ts           # Entry point
├── server.ts          # MCP Server setup and tool registration
├── tools/             # Tool handlers
├── core/              # Business logic engines
├── schemas/           # Zod schemas
├── rules/             # JSON rule files
└── utils/             # Utility functions

License

MIT

Structured Outputs

Each tool returns human-readable Markdown in content and machine-readable JSON in structuredContent.

Example: spec_interrogate structured output

{
  "readiness": {
    "score": 35,
    "status": "Not Ready",
    "fields": { ... }
  },
  "clarification": {
    "missingFields": ["target_user", "data_persistence"],
    "questions": [
      {
        "field": "target_user",
        "question": "目标用户是谁？",
        "whyImportant": "决定 UI 风格、交互复杂度、技术选型",
        "options": ["个人用户", "小团队", "企业用户"],
        "defaultAssumption": "个人用户",
        "priority": "P0"
      }
    ],
    "defaultAssumptions": { ... }
  },
  "recommendation": {
    "canProceed": false,
    "suggestedNextTool": "spec_interrogate",
    "reason": "信息不足，需要先回答追问"
  }
}

Key structured fields:

示例

示例 1：报名系统

输入：

{
  "raw_idea": "我想做一个报名系统，用户可以提交资料，后台能看到所有报名信息并审核",
  "scenario": "build_product",
  "target_platform": "web"
}

推荐流程：

1. 先调用 spec_interrogate，会追问：目标用户是谁？是否需要登录？是否需要保存数据？

2. 补充信息后调用 spec_compile，生成产品规格和开发 Prompt

3. 调用 architecture_decide，判断是否需要后端和数据库

4. 调用 acceptance_generate，生成验收清单

示例 2：展示官网

输入：

{
  "raw_idea": "做一个产品展示官网，只需要静态展示",
  "scenario": "build_product",
  "target_platform": "web"
}

预期行为：

• architecture_decide 会推荐纯前端架构，不推荐数据库

• spec_compile 会生成简洁的静态站点规格

• 不会输出伪 API 设计