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mcp-builderMCP 构建器

Agent Skill

mcp-builder 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

8,739

周安装

357

GitHub Stars

1

下载量

2,827
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:mcp-builder(MCP 构建器)
来源仓库:https://github.com/mcp-use/skills
仓库路径:skills/mcp-builder
安装命令:
npx skills add https://github.com/mcp-use/skills --skill mcp-builder
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/mcp-use/skills --skill mcp-builder

简介

使用 mcp-use 框架和自动小部件注册构建生产就绪的 MCP 服务器。

  • 使用 npx create-mcp-use-app 进行引导
  • 并从三个模板中进行选择:starter(全功能)、mcp-apps(ChatGPT 优化)或空白(最小)
  • 使用具有自动验证和清晰参数描述的 Zod 架构定义工具、资源和提示
  • 自动从 resources/ 注册 React 小部件
  • 文件夹作为 MCP 工具和资源,具有对 MCP 应用程序和 ChatGPT 的双协议支持
  • 使用热重载进行本地测试,使用位于 http://localhost:3000/inspector 的 Inspector UI,并在使用 mcp-use start --tunnel 进行部署之前通过隧道连接到 ChatGPT
  • 使用yarn部署部署到mcp-use云
  • 用于自动缩放、启用 HTTPS 的托管,并实现零停机更新

SKILL.md

MCP Server Builder

Build production-ready MCP servers with the mcp-use framework. This Skill provides quick-start instructions and best practices for creating MCP servers.

Quick Start

Always bootstrap with npx create-mcp-use-app:

npx create-mcp-use-app my-mcp-server
cd my-mcp-server

Choose template based on needs:

  • --template starter - Full-featured with all MCP primitives (tools, resources, prompts) + example widgets
  • --template mcp-apps - Optimized for ChatGPT widgets with product search example
  • --template blank - Minimal starting point for custom implementation
# Example: MCP Apps template
npx create-mcp-use-app my-server --template mcp-apps
cd my-server
yarn install

Template Details:

  • starter: Best for learning - includes all MCP features plus widgets
  • mcp-apps: Best for ChatGPT apps - includes product carousel/accordion example
  • blank: Best for experts - minimal boilerplate

MCP Apps Structure

Automatic Widget Registration

The mcp-apps and starter templates automatically discover and register React widgets from the resources/ folder:

Single-file widget pattern:

resources/
└── weather-display.tsx  # Widget name becomes "weather-display"

Folder-based widget pattern:

resources/
└── product-search/      # Widget name becomes "product-search"
    ├── widget.tsx       # Entry point (required name!)
    ├── components/      # Sub-components
    ├── hooks/           # Custom hooks
    ├── types.ts
    └── constants.ts

What happens automatically:

  1. Server scans resources/ folder at startup
  2. Finds .tsx files or widget.tsx in folders
  3. Extracts widgetMetadata from each component
  4. Registers as MCP Tool (e.g., weather-display)
  5. Registers as MCP Resource (e.g., ui://widget/weather-display.html)
  6. Builds widget bundles with Vite

No manual registration needed! Just export widgetMetadata and a default component.

Defining Tools

Tools are executable functions that AI models can call:

import { MCPServer, text, object } from "mcp-use/server";
import { z } from "zod";

const server = new MCPServer({
  name: "my-server",
  version: "1.0.0",
  description: "My MCP server"
});

// Simple tool
server.tool(
  {
    name: "greet-user",
    description: "Greet a user by name",
    schema: z.object({
      name: z.string().describe("The user's name"),
      formal: z.boolean().optional().describe("Use formal greeting")
    })
  },
  async ({ name, formal }) => {
    const greeting = formal ? `Good day, ${name}` : `Hey ${name}!`;
    return text(greeting);
  }
);

Key points:

  • Use Zod for schema validation
  • Add .describe() to all parameters
  • Return appropriate response types (text, object, widget)

Defining Resources

Resources expose data that clients can read:

import { object, text, markdown } from "mcp-use/server";

// Static resource
server.resource(
  {
    uri: "config://settings",
    name: "Application Settings",
    description: "Current configuration",
    mimeType: "application/json"
  },
  async () => {
    return object({
      theme: "dark",
      version: "1.0.0"
    });
  }
);

// Dynamic resource
server.resource(
  {
    uri: "stats://current",
    name: "Current Stats",
    description: "Real-time statistics",
    mimeType: "application/json"
  },
  async () => {
    const stats = await getStats();
    return object(stats);
  }
);

// Markdown resource
server.resource(
  {
    uri: "docs://guide",
    name: "User Guide",
    description: "Documentation",
    mimeType: "text/markdown"
  },
  async () => {
    return markdown("# Guide\n\nWelcome!");
  }
);

Response helpers available:

  • text(string) - Plain text
  • object(data) - JSON objects
  • markdown(string) - Markdown content
  • html(string) - HTML content
  • image(buffer, mimeType) - Binary images
  • audio(buffer, mimeType) - Audio files
  • binary(buffer, mimeType) - Binary data
  • mix(...contents) - Combine multiple content types

Advanced response examples:

// Audio response
import { audio } from 'mcp-use/server';

// From base64 data
return audio(base64Data, "audio/wav");

// From file path (async)
return await audio("/path/to/audio.mp3");

// Binary data (PDFs, etc.)
import { binary } from 'mcp-use/server';
return binary(pdfBuffer, "application/pdf");

// Mix multiple content types
import { mix, text, object, resource } from 'mcp-use/server';
return mix(
  text("Analysis complete:"),
  object({ score: 95, status: "pass" }),
  resource("report://analysis-123", text("Full report..."))
);

Defining Prompts

Prompts are reusable templates for AI interactions:

server.prompt(
  {
    name: "code-review",
    description: "Generate a code review template",
    schema: z.object({
      language: z.string().describe("Programming language"),
      focusArea: z.string().optional().describe("Specific focus area")
    })
  },
  async ({ language, focusArea }) => {
    const focus = focusArea ? ` with focus on ${focusArea}` : "";
    return {
      messages: [
        {
          role: "user",
          content: {
            type: "text",
            text: `Please review this ${language} code${focus}.`
          }
        }
      ]
    };
  }
);

Testing Locally

Development mode (hot reload):

yarn dev

Production mode:

yarn build
yarn start

Inspector UI: Access at http://localhost:3000/inspector to test tools, view resources, and try prompts.

Tunneling (test with ChatGPT before deploying):

Option 1 - Auto-tunnel:

mcp-use start --port 3000 --tunnel

Option 2 - Separate tunnel:

yarn start  # Terminal 1
npx @mcp-use/tunnel 3000  # Terminal 2

You'll get a public URL like https://happy-cat.local.mcp-use.run/mcp

Tunnel details:

  • Expires after 24 hours
  • Closes after 1 hour of inactivity
  • Rate limit: 10 creations/hour, max 5 active per IP

Learn more: https://mcp-use.com/docs/tunneling

Deployment

Deploy to mcp-use Cloud (recommended):

# Login first (if not already)
npx mcp-use login

# Deploy
yarn deploy

If authentication error:

npx mcp-use login
yarn deploy

After deployment:

  • Public URL provided (e.g., https://your-server.mcp-use.com/mcp)
  • Auto-scaled and monitored
  • HTTPS enabled
  • Zero-downtime deployments

Best Practices

Tool Design:

  • ✅ One tool = one focused capability
  • ✅ Descriptive names and descriptions
  • ✅ Use .describe() on all Zod fields
  • ✅ Handle errors gracefully
  • ✅ Return helpful error messages

Resource Design:

  • ✅ Use clear URI schemes (config://, docs://, stats://)
  • ✅ Choose appropriate MIME types
  • ✅ Use response helpers for cleaner code
  • ✅ Make resources dynamic when needed

Prompt Design:

  • ✅ Keep prompts reusable
  • ✅ Use system messages for context
  • ✅ Parameterize with Zod schemas
  • ✅ Include clear instructions

Testing:

  • ✅ Test with Inspector UI first
  • ✅ Use tunneling to test with real clients before deploying
  • ✅ Verify all tools, resources, and prompts work as expected

Deployment:

  • ✅ Test locally and with tunneling first
  • ✅ Run npx mcp-use login if deploy fails
  • ✅ Version your server semantically
  • ✅ Document breaking changes

Widget Support

Automatic Widget Registration

When using the mcp-apps or starter template, widgets in the resources/ folder are automatically registered:

// resources/weather-display.tsx
import { useWidget, McpUseProvider, type WidgetMetadata } from 'mcp-use/react';
import { z } from 'zod';

const propSchema = z.object({
  city: z.string(),
  temperature: z.number()
});

// Required: Export widget metadata
export const widgetMetadata: WidgetMetadata = {
  description: "Display weather information",
  props: propSchema, // Use 'props', not 'schema'!
};

// Required: Export default component
export default function WeatherDisplay() {
  const { props, isPending } = useWidget<z.infer<typeof propSchema>>();

  // Always handle loading state
  if (isPending) return <div>Loading...</div>;

  return (
    <McpUseProvider autoSize>
      <div>
        <h2>{props.city}</h2>
        <p>{props.temperature}°C</p>
      </div>
    </McpUseProvider>
  );
}

Widget automatically becomes available as:

  • MCP Tool: weather-display
  • MCP Resource: ui://widget/weather-display.html

Content Security Policy (CSP)

Control what external resources widgets can access:

export const widgetMetadata: WidgetMetadata = {
  description: "Weather widget",
  props: z.object({ city: z.string() }),
  metadata: {
    csp: {
      // APIs to call
      connectDomains: ["https://api.weather.com"],
      // Static assets to load
      resourceDomains: ["https://cdn.weather.com"],
      // Iframes to embed
      frameDomains: ["https://embed.weather.com"],
      // Script directives
      scriptDirectives: ["'unsafe-inline'"],
    },
  },
};

Alternatively, set at server level:

server.uiResource({
  type: "mcpApps",
  name: "my-widget",
  htmlTemplate: `...`,
  metadata: {
    csp: {
      connectDomains: ["https://api.example.com"],
      resourceDomains: ["https://cdn.example.com"],
    },
  },
});

Dual-Protocol Widget Support

mcp-use supports the MCP Apps standard (SEP-1865) with automatic dual-protocol support:

import { MCPServer } from 'mcp-use/server';

const server = new MCPServer({
  name: 'my-server',
  version: '1.0.0',
  baseUrl: process.env.MCP_URL || 'http://localhost:3000', // Required for widgets
});

// Register a dual-protocol widget
server.uiResource({
  type: "mcpApps", // Works with BOTH MCP Apps clients AND ChatGPT
  name: "weather-display",
  htmlTemplate: `<!DOCTYPE html>...`,
  metadata: {
    csp: { connectDomains: ["https://api.weather.com"] },
    prefersBorder: true,
    autoResize: true,
  },
});

What happens automatically:

  • MCP Apps clients (Claude, Goose) receive: text/html;profile=mcp-app with _meta.ui.*
  • ChatGPT receives: text/html+skybridge with _meta.openai/*
  • Same widget code works everywhere!

Custom OpenAI Metadata

Need ChatGPT-specific features? Combine both metadata fields:

server.uiResource({
  type: "mcpApps",
  name: "my-widget",
  htmlTemplate: `...`,
  // Unified metadata (dual-protocol)
  metadata: {
    csp: { connectDomains: ["https://api.example.com"] },
    prefersBorder: true,
  },
  // ChatGPT-specific overrides
  appsSdkMetadata: {
    "openai/widgetDescription": "ChatGPT-specific description",
    "openai/customFeature": "some-value", // Any custom OpenAI metadata
  },
});

Project Structure

my-mcp-server/
├── resources/           # React widgets (apps-sdk)
│   └── widget.tsx
├── public/             # Static assets
├── index.ts            # Server entry point
├── package.json
├── tsconfig.json
└── README.md

Common Patterns

Tool with dual-protocol widget:

import { MCPServer, widget, text } from 'mcp-use/server';
import { z } from 'zod';

const server = new MCPServer({
  name: 'my-server',
  version: '1.0.0',
  baseUrl: process.env.MCP_URL || 'http://localhost:3000',
});

server.tool(
  {
    name: "show-data",
    description: "Display data with visualization",
    schema: z.object({
      query: z.string()
    }),
    widget: {
      name: "data-display", // Must exist in resources/
      invoking: "Loading...",
      invoked: "Data loaded"
    }
  },
  async ({ query }) => {
    const data = await fetchData(query);
    return widget({
      props: { data },
      output: text(`Found ${data.length} results`)
    });
  }
);

Resource template (parameterized):

server.resourceTemplate(
  {
    uriTemplate: "user://{userId}/profile",
    name: "User Profile",
    description: "Get user by ID",
    mimeType: "application/json"
  },
  async ({ userId }) => {
    const user = await fetchUser(userId);
    return object(user);
  }
);

Error handling:

server.tool(
  {
    name: "divide",
    schema: z.object({
      a: z.number(),
      b: z.number()
    })
  },
  async ({ a, b }) => {
    if (b === 0) {
      return text("Error: Cannot divide by zero");
    }
    return text(`Result: ${a / b}`);
  }
);

Detailed Examples

For comprehensive examples and advanced patterns, connect to the mcp-use MCP server which provides:

  • Complete example resources for all primitives
  • Full working server examples
  • Detailed documentation
  • Interactive widgets showcase

Learn More

Quick Reference

Commands:

  • npx create-mcp-use-app my-server - Bootstrap
  • yarn dev - Development mode
  • yarn build - Build for production
  • yarn start - Run production server
  • mcp-use start --tunnel - Start with tunnel
  • npx mcp-use login - Authenticate
  • yarn deploy - Deploy to cloud

Response helpers:

  • text(str), object(data), markdown(str), html(str)
  • image(buf, mime), audio(buf, mime), binary(buf, mime)
  • mix(...) - Combine multiple content types
  • widget({props, output}) - Return widget with data

Server methods:

  • server.tool() - Define executable tool
  • server.resource() - Define static/dynamic resource
  • server.resourceTemplate() - Define parameterized resource
  • server.prompt() - Define prompt template
  • server.uiResource() - Define widget resource
  • server.listen() - Start server

Widget metadata fields:

  • description - Widget description
  • props - Zod schema for widget props
  • metadata - Unified config (dual-protocol)
  • metadata.csp - Content Security Policy
  • appsSdkMetadata - ChatGPT-specific overrides

Available templates:

  • starter - Full-featured (tools, resources, prompts, widgets)
  • mcp-apps - ChatGPT-optimized with product example
  • blank - Minimal boilerplate

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Claude Code

29.76%
按下载量换算841

Codex

25.75%
按下载量换算728

OpenCode

17.66%
按下载量换算499

Antigravity

12.22%
按下载量换算345

Cursor

7.01%
按下载量换算198

trae

3.71%
按下载量换算105

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/mcp-use/skills --skill mcp-builder;npx skills add mcp-use/skills --skill "mcp-builder" 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。

来源信息

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