什么是 MCP Server Builder？

MCP Server Builder 是一个旨在帮助开发人员创建高质量模型上下文协议 (MCP) 服务器的技术框架。它提供了一种结构化方法，使大语言模型能够通过设计良好的工具与外部服务进行交互。通过利用 Openclaw Skills 生态系统中的这一资源，开发人员可以确保其 AI 智能体拥有必要的上下文、清晰的工具命名规范以及复杂现实任务执行所需的可操作错误消息。

该技能支持 TypeScript 和 Python 环境，为 MCP SDK 提供深度集成模式。它优先考虑 API 覆盖率和工作流效率，确保构建的任何服务器都遵循传输机制的行业最佳实践，例如用于本地环境的 stdio 和用于远程部署的可流式 HTTP。

下载入口:https://github.com/openclaw/skills/tree/main/skills/uniquevme/unique-mcp-builder-test

安装与下载

1. ClawHub CLI

从源直接安装技能的最快方式。

2. 手动安装

将技能文件夹复制到以下位置之一

优先级：工作区 > 本地 > 内置

3. 提示词安装

将此提示词复制到 OpenClaw 即可自动安装。

MCP Server Builder 应用场景

• 开发自定义 API 连接器，允许 AI 智能体与专有的内部服务交互。
• 为 GitHub 或数据库管理系统等流行平台构建专门的工作流工具。
• 通过为多智能体系统添加结构化工具定义，增强 Openclaw Skills 的能力。
• 创建自动化评估套件，以验证生产 AI 应用中工具调用的可靠性。

1. 深入研究目标服务的 API 文档和身份验证要求。
2. 选择技术栈，例如具有静态类型优势的 TypeScript 或具有快速原型设计能力的 Python。
3. 实现核心基础设施，包括用于身份验证、错误处理和分页的共享实用程序。
4. 使用 Zod 或 Pydantic 等结构化架构定义工具，以确保输入验证和清晰的参数说明。
5. 使用 MCP Inspector 测试实现，以验证协议合规性和工具发现。
6. 生成包含 10 个复杂 QA 对的全面评估 XML，以基准测试大语言模型的有效性。

MCP Server Builder 配置指南

要开始将此技能作为 Openclaw Skills 开发工作流的一部分使用，请使用所需的 SDK 初始化您的项目：

# 对于基于 TypeScript 的 MCP 服务器
npm install @modelcontextprotocol/sdk zod

# 对于基于 Python 的 MCP 服务器
pip install mcp pydantic

# 测试您的服务器
npx @modelcontextprotocol/inspector 

MCP Server Builder 数据架构与分类体系

MCP Server Builder 通过结构化架构和元数据组织数据，以优化智能体交互。以下是这些 Openclaw Skills 中的数据结构方式：

name: mcp-builder
description: Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
license: Complete terms in LICENSE.txt

MCP Server Development Guide

Overview

Create MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. The quality of an MCP server is measured by how well it enables LLMs to accomplish real-world tasks.

Process

?? High-Level Workflow

Creating a high-quality MCP server involves four main phases:

Phase 1: Deep Research and Planning

1.1 Understand Modern MCP Design

API Coverage vs. Workflow Tools: Balance comprehensive API endpoint coverage with specialized workflow tools. Workflow tools can be more convenient for specific tasks, while comprehensive coverage gives agents flexibility to compose operations. Performance varies by client—some clients benefit from code execution that combines basic tools, while others work better with higher-level workflows. When uncertain, prioritize comprehensive API coverage.

Tool Naming and Discoverability: Clear, descriptive tool names help agents find the right tools quickly. Use consistent prefixes (e.g., github_create_issue, github_list_repos) and action-oriented naming.

Context Management: Agents benefit from concise tool descriptions and the ability to filter/paginate results. Design tools that return focused, relevant data. Some clients support code execution which can help agents filter and process data efficiently.

Actionable Error Messages: Error messages should guide agents toward solutions with specific suggestions and next steps.

1.2 Study MCP Protocol Documentation

Navigate the MCP specification:

Start with the sitemap to find relevant pages: https://modelcontextprotocol.io/sitemap.xml

Then fetch specific pages with .md suffix for markdown format (e.g., https://modelcontextprotocol.io/specification/draft.md).

Key pages to review:

• Specification overview and architecture
• Transport mechanisms (streamable HTTP, stdio)
• Tool, resource, and prompt definitions

1.3 Study Framework Documentation

Recommended stack:

• Language: TypeScript (high-quality SDK support and good compatibility in many execution environments e.g. MCPB. Plus AI models are good at generating TypeScript code, benefiting from its broad usage, static typing and good linting tools)
• Transport: Streamable HTTP for remote servers, using stateless JSON (simpler to scale and maintain, as opposed to stateful sessions and streaming responses). stdio for local servers.

Load framework documentation:

• MCP Best Practices: ?? View Best Practices - Core guidelines

For TypeScript (recommended):

• TypeScript SDK: Use WebFetch to load https://raw.githubusercontent.com/modelcontextprotocol/typescript-sdk/main/README.md
• ? TypeScript Guide - TypeScript patterns and examples

For Python:

• Python SDK: Use WebFetch to load https://raw.githubusercontent.com/modelcontextprotocol/python-sdk/main/README.md
• ?? Python Guide - Python patterns and examples

1.4 Plan Your Implementation

Understand the API: Review the service's API documentation to identify key endpoints, authentication requirements, and data models. Use web search and WebFetch as needed.

Tool Selection: Prioritize comprehensive API coverage. List endpoints to implement, starting with the most common operations.

Phase 2: Implementation

2.1 Set Up Project Structure

See language-specific guides for project setup:

• ? TypeScript Guide - Project structure, package.json, tsconfig.json
• ?? Python Guide - Module organization, dependencies

2.2 Implement Core Infrastructure

Create shared utilities:

• API client with authentication
• Error handling helpers
• Response formatting (JSON/Markdown)
• Pagination support

2.3 Implement Tools

For each tool:

Input Schema:

• Use Zod (TypeScript) or Pydantic (Python)
• Include constraints and clear descriptions
• Add examples in field descriptions

Output Schema:

• Define outputSchema where possible for structured data
• Use structuredContent in tool responses (TypeScript SDK feature)
• Helps clients understand and process tool outputs

Tool Description:

• Concise summary of functionality
• Parameter descriptions
• Return type schema

Implementation:

• Async/await for I/O operations
• Proper error handling with actionable messages
• Support pagination where applicable
• Return both text content and structured data when using modern SDKs

Annotations:

• readOnlyHint: true/false
• destructiveHint: true/false
• idempotentHint: true/false
• openWorldHint: true/false

Phase 3: Review and Test

3.1 Code Quality

Review for:

• No duplicated code (DRY principle)
• Consistent error handling
• Full type coverage
• Clear tool descriptions

3.2 Build and Test

TypeScript:

• Run npm run build to verify compilation
• Test with MCP Inspector: npx @modelcontextprotocol/inspector

Python:

• Verify syntax: python -m py_compile your_server.py
• Test with MCP Inspector

See language-specific guides for detailed testing approaches and quality checklists.

Phase 4: Create Evaluations

After implementing your MCP server, create comprehensive evaluations to test its effectiveness.

Load ? Evaluation Guide for complete evaluation guidelines.

4.1 Understand Evaluation Purpose

Use evaluations to test whether LLMs can effectively use your MCP server to answer realistic, complex questions.

4.2 Create 10 Evaluation Questions

To create effective evaluations, follow the process outlined in the evaluation guide:

1. Tool Inspection: List available tools and understand their capabilities
2. Content Exploration: Use READ-ONLY operations to explore available data
3. Question Generation: Create 10 complex, realistic questions
4. Answer Verification: Solve each question yourself to verify answers

4.3 Evaluation Requirements

Ensure each question is:

• Independent: Not dependent on other questions
• Read-only: Only non-destructive operations required
• Complex: Requiring multiple tool calls and deep exploration
• Realistic: Based on real use cases humans would care about
• Verifiable: Single, clear answer that can be verified by string comparison
• Stable: Answer won't change over time

4.4 Output Format

Create an XML file with this structure:

  
    Find discussions about AI model launches with animal codenames. One model needed a specific safety designation that uses the format ASL-X. What number X was being determined for the model named after a spotted wild cat?
    3
  

Reference Files

?? Documentation Library

Load these resources as needed during development:

Core MCP Documentation (Load First)

• MCP Protocol: Start with sitemap at https://modelcontextprotocol.io/sitemap.xml, then fetch specific pages with .md suffix
• ?? MCP Best Practices - Universal MCP guidelines including:
  • Server and tool naming conventions
  • Response format guidelines (JSON vs Markdown)
  • Pagination best practices
  • Transport selection (streamable HTTP vs stdio)
  • Security and error handling standards

SDK Documentation (Load During Phase 1/2)

• Python SDK: Fetch from https://raw.githubusercontent.com/modelcontextprotocol/python-sdk/main/README.md
• TypeScript SDK: Fetch from https://raw.githubusercontent.com/modelcontextprotocol/typescript-sdk/main/README.md

Language-Specific Implementation Guides (Load During Phase 2)

• ?? Python Implementation Guide - Complete Python/FastMCP guide with:

  • Server initialization patterns
  • Pydantic model examples
  • Tool registration with @mcp.tool
  • Complete working examples
  • Quality checklist

• ? TypeScript Implementation Guide - Complete TypeScript guide with:

  • Project structure
  • Zod schema patterns
  • Tool registration with server.registerTool
  • Complete working examples
  • Quality checklist

Evaluation Guide (Load During Phase 4)

• ? Evaluation Guide - Complete evaluation creation guide with:
  • Question creation guidelines
  • Answer verification strategies
  • XML format specifications
  • Example questions and answers
  • Running an evaluation with the provided scripts