安装方式
手动下载安装
下载 ZIP 后解压到技能目录即可安装。若在桌面客户端 WebView中直接下载出现异常,本站会改为提示页 + 原始链接,请按页内说明操作。
下载 ZIP (oss-anthropic-mcp-builder-v1.0.0.zip)触发指令
/mcp-builder
跨平台安装指引
该技能声明兼容以下 1 个平台,将 ZIP 解压到对应目录即可被识别。
unzip oss-anthropic-mcp-builder-v1.0.0.zip -d ~/.claude/skills/
mkdir -p 创建;启用 Skill 后请重启对应 Agent 让配置生效。
使用指南
MCP 服务器开发指南
概述
构建 MCP(Model Context Protocol)服务器,使大模型能通过设计良好的 工具(tools) 与外部服务交互。服务器质量取决于:模型能否借助你的工具 稳定完成真实任务。
流程
高层工作流
高质量 MCP 服务器大致分四阶段:
阶段一:深入调研与规划
1.1 理解当代 MCP 设计
API 覆盖面 vs. 工作流型工具:
在「尽可能覆盖 API」与「为特定任务封装高层工具」之间取舍。工作流工具对固定场景更省事,广覆盖则让智能体自由组合调用。不同客户端表现不同——有的适合配合代码执行拼基础工具,有的更适合高层工作流。不确定时,优先广覆盖 API。
工具命名与可发现性:
名称要清晰、可检索;建议统一前缀(如 github_create_issue、github_list_repos),动词开头、表意明确。
上下文管理:
工具描述宜简洁;结果宜可分页、可筛选。部分客户端支持代码执行,便于智能体在本地过滤数据。
可行动的错误信息:
错误应指出 下一步 与 如何修正,而不是只抛栈。
1.2 阅读 MCP 协议文档
从站点地图入手:https://modelcontextprotocol.io/sitemap.xml
具体页面可加 .md 后缀取 Markdown(如 https://modelcontextprotocol.io/specification/draft.md)。
重点包括:规范总览与架构、传输方式(streamable HTTP、stdio)、工具 / 资源 / 提示词定义等。
1.3 阅读框架文档
推荐栈:
- 语言:TypeScript(SDK 成熟、运行环境兼容性好,且模型写 TS 质量通常较高)
- 传输:远程服务用 Streamable HTTP(无状态 JSON,易扩展维护);本地服务可用 stdio。
文档加载:
- MCP 最佳实践:本技能包内
./reference/mcp_best_practices.md - TypeScript:可拉取
https://raw.githubusercontent.com/modelcontextprotocol/typescript-sdk/main/README.md,并参阅./reference/node_mcp_server.md - Python:可拉取
https://raw.githubusercontent.com/modelcontextprotocol/python-sdk/main/README.md,并参阅./reference/python_mcp_server.md
1.4 规划实现
理解 API: 阅读服务 API 文档,弄清端点、鉴权、数据模型;可用联网搜索与按需抓取页面。
工具选择: 仍以广覆盖为主;列出待实现端点,从最高频操作开始。
阶段二:实现
2.1 项目结构
见语言指南:./reference/node_mcp_server.md、./reference/python_mcp_server.md(目录、package.json/pyproject 等)。
2.2 核心基础设施
共用能力建议包括:带鉴权的 API 客户端、错误处理辅助、响应格式化(JSON/Markdown)、分页支持等。
2.3 实现工具
对每个工具:
输入 Schema: TypeScript 用 Zod,Python 用 Pydantic;约束写清楚,字段描述里可给示例。
输出 Schema: 尽量定义 outputSchema;TypeScript SDK 可在响应里用 structuredContent,便于客户端解析。
工具描述: 功能摘要、参数说明、返回结构。
实现要点: I/O 用 async;错误信息可行动;需要时分页;现代 SDK 下可同时返回文本与结构化内容。
注解(Annotations): readOnlyHint、destructiveHint、idempotentHint、openWorldHint 等按语义设置。
阶段三:评审与测试
3.1 代码质量
避免重复(DRY)、错误处理一致、类型覆盖完整、工具描述清晰。
3.2 构建与测试
TypeScript: npm run build;用 MCP Inspector:npx @modelcontextprotocol/inspector
Python: python -m py_compile ...;同样可用 MCP Inspector
细节与检查清单见各语言指南。
阶段四:编写评测(Evaluations)
实现完成后,应用评测检验 真实复杂问题下模型能否用好你的服务器。
完整说明见 ./reference/evaluation.md。
4.1 评测目的
验证:在给定 MCP 下,模型能否完成 贴近真实、需要多步调用 的任务。
4.2 编写 10 道评测题
按评测指南流程:
- 工具巡检:列出工具并理解能力
- 内容探索:用 只读 操作探索数据
- 出题:编写 10 道复杂、贴近真实的题目
- 自解验证:自行解答以确认标答
4.3 题目要求
每道题应:相互独立、只读/非破坏、足够复杂(多工具、深探索)、贴近真实用例、答案唯一可字符串比对验证、答案稳定(不随时间漂移)。
4.4 输出格式
使用如下 XML 结构(示例):
<evaluation>
<qa_pair>
<question>……</question>
<answer>……</answer>
</qa_pair>
</evaluation>
参考文件
文档库(按需加载)
MCP 核心(优先)
- 协议:从
https://modelcontextprotocol.io/sitemap.xml导航,页面用.md后缀 ./reference/mcp_best_practices.md:命名、响应格式、分页、传输选择、安全与错误等通用规范
SDK(阶段一/二)
- Python / TypeScript SDK README(见上文 raw 地址)
语言实现指南(阶段二)
./reference/python_mcp_server.md:FastMCP、Pydantic、@mcp.tool等./reference/node_mcp_server.md:Zod、server.registerTool等
评测(阶段四)
./reference/evaluation.md:出题、验证、XML 格式、脚本运行方式等
# 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](./reference/mcp_best_practices.md) - Core guidelines
**For TypeScript (recommended):**
- **TypeScript SDK**: Use WebFetch to load `https://raw.githubusercontent.com/modelcontextprotocol/typescript-sdk/main/README.md`
- [⚡ TypeScript Guide](./reference/node_mcp_server.md) - TypeScript patterns and examples
**For Python:**
- **Python SDK**: Use WebFetch to load `https://raw.githubusercontent.com/modelcontextprotocol/python-sdk/main/README.md`
- [🐍 Python Guide](./reference/python_mcp_server.md) - 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](./reference/node_mcp_server.md) - Project structure, package.json, tsconfig.json
- [🐍 Python Guide](./reference/python_mcp_server.md) - 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](./reference/evaluation.md) 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:
```xml
<evaluation>
<qa_pair>
<question>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?</question>
<answer>3</answer>
</qa_pair>
<!-- More qa_pairs... -->
</evaluation>
```
---
# 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](./reference/mcp_best_practices.md) - 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](./reference/python_mcp_server.md) - Complete Python/FastMCP guide with:
- Server initialization patterns
- Pydantic model examples
- Tool registration with `@mcp.tool`
- Complete working examples
- Quality checklist
- [⚡ TypeScript Implementation Guide](./reference/node_mcp_server.md) - 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](./reference/evaluation.md) - 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