어떤 스킬인가요?
외부 서비스의 기능을 AI가 사용할 도구로 연결할 때 쓰는 개발 스킬입니다. API가 있더라도 어떤 입력으로 무엇을 호출해야 하는지 AI가 이해할 수 있는 도구 정의가 필요합니다.
연결하려는 API 문서와 수행할 작업을 정리해보세요. 필요한 기능의 범위를 나누고 MCP 서버 코드와 도구 구조를 설계하는 데 활용할 수 있습니다.
mcp-builder · Claude / Claude Code
API를 AI가 호출할 수 있도록 MCP 서버와 도구를 설계합니다.
외부 서비스의 기능을 AI가 사용할 도구로 연결할 때 쓰는 개발 스킬입니다. API가 있더라도 어떤 입력으로 무엇을 호출해야 하는지 AI가 이해할 수 있는 도구 정의가 필요합니다.
연결하려는 API 문서와 수행할 작업을 정리해보세요. 필요한 기능의 범위를 나누고 MCP 서버 코드와 도구 구조를 설계하는 데 활용할 수 있습니다.
“우리 업무 API의 프로젝트 조회 기능을 MCP 도구로 만들고 싶어. 필요한 입력과 반환 정보를 정하고 서버 코드를 구성해줘.”
받을 수 있는 결과MCP 서버 코드와 도구 정의
해당 스킬을 추가할 수 있는 Claude 환경이 필요합니다. 파일·코드 작업은 실행 도구 지원 여부를 확인하세요.
Claude Code 채팅에 설치 요청을 붙여넣으세요. 개인용은 ~/.claude/skills/, 프로젝트용은 .claude/skills/ 아래에 스킬 폴더와 보조 파일을 함께 둡니다. Claude 웹에서는 지원되는 계정의 Skills 설정에서 패키지를 추가하세요.
https://github.com/anthropics/skills 에서 skills/mcp-builder 스킬을 현재 에이전트의 스킬 폴더에 설치해줘. SKILL.md와 이 스킬이 참조하는 scripts·references·assets와 공통 파일을 함께 유지하고, 필요한 실행 환경과 추가 연결을 확인한 뒤 알려줘. 설치가 끝나면 mcp-builder 스킬을 인식하는지 확인해줘.
“mcp-builder 스킬을 사용해줘. 입력 자료: 연결할 API와 필요한 작업”처럼 요청하세요. 위의 예시를 내 자료에 맞게 바꿔도 좋아요.
예상 결과는 ‘MCP 서버 코드와 도구 정의’입니다. 필요한 내용이 포함됐는지 확인하세요. 입력 자료의 사실·숫자와 결과를 대조하고, 부족한 부분을 이어서 요청하세요.
제작자 원본 2026-10-04 확인 · 설치 안내 2026-10-04 확인 · 실제 설치·실행 미검증
모델 사용 요금과 연결 앱 요금은 이용 중인 서비스의 정책을 따릅니다.
설치 안내 출처--- 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:**--- 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](./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
Original skill: anthropics/skills/skills/mcp-builder/SKILL.md
Source: https://github.com/anthropics/skills/blob/8a1541c4a3ffa5a20a5a91de0dcf3f0bab1d1ef4/skills/mcp-builder/SKILL.md
License: Apache-2.0
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limitations under the License.SKILL.md 본문입니다. 원문에서 참조하는 스크립트·보조 파일은 원본 패키지에 포함되어 있습니다.