[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6XEF3HNJ0YR32MMHG6DTVQQ":3,"skill-same:01M6XEF3HNJ0YR32MMHG6DTVQQ":112},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":16,"sourceHtmlUrl":17,"categories":18,"keywords":24,"publishedAt":45,"linkOnly":46,"markdownContent":47,"markdownBody":48,"frontMatter":49,"refName":51,"commitSha":52,"skillDirectory":53,"forksCount":14,"repositoryHtmlUrl":54,"repositoryHomepageUrl":6,"ownerHtmlUrl":55,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":56,"install":57,"resources":70},"01M6XEF3HNJ0YR32MMHG6DTVQQ","mcp-builder",null,"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\u002FTypeScript (MCP SDK).","fadrienne\u002FMyGitHub","fadrienne","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F197874571?v=4","01M6X64V8VWCRSMFZ4EPS2BWXG","Francesca Adrienne","01M6X64VD5MW4F9PHDWQKS7ZAF",0,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fawesome-claude-skills\u002Fmcp-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fawesome-claude-skills\u002Fmcp-builder\u002FSKILL.md",[19],{"categoryNo":20,"categoryName":21,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44],"mcp builder","MCP","builder","when","for","with","building mcp servers","mcp sdk","integrate","use","guide","context","node","model","Tools","fastmcp","model context protocol","llms","python","typescript","2026-11-10 10:12:30",false,"---\nname: mcp-builder\ndescription: 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\u002FTypeScript (MCP SDK).\nlicense: Complete terms in LICENSE.txt\n---\n\n# MCP Server Development Guide\n\n## Overview\n\nTo create high-quality MCP (Model Context Protocol) servers that enable LLMs to effectively interact with external services, use this skill. An MCP server provides tools that allow LLMs to access external services and APIs. The quality of an MCP server is measured by how well it enables LLMs to accomplish real-world tasks using the tools provided.\n\n---\n\n# Process\n\n## ? High-Level Workflow\n\nCreating a high-quality MCP server involves four main phases:\n\n### Phase 1: Deep Research and Planning\n\n#### 1.1 Understand Agent-Centric Design Principles\n\nBefore diving into implementation, understand how to design tools for AI agents by reviewing these principles:\n\n**Build for Workflows, Not Just API Endpoints:**\n- Don't simply wrap existing API endpoints - build thoughtful, high-impact workflow tools\n- Consolidate related operations (e.g., `schedule_event` that both checks availability and creates event)\n- Focus on tools that enable complete tasks, not just individual API calls\n- Consider what workflows agents actually need to accomplish\n\n**Optimize for Limited Context:**\n- Agents have constrained context windows - make every token count\n- Return high-signal information, not exhaustive data dumps\n- Provide \"concise\" vs \"detailed\" response format options\n- Default to human-readable identifiers over technical codes (names over IDs)\n- Consider the agent's context budget as a scarce resource\n\n**Design Actionable Error Messages:**\n- Error messages should guide agents toward correct usage patterns\n- Suggest specific next steps: \"Try using filter='active_only' to reduce results\"\n- Make errors educational, not just diagnostic\n- Help agents learn proper tool usage through clear feedback\n\n**Follow Natural Task Subdivisions:**\n- Tool names should reflect how humans think about tasks\n- Group related tools with consistent prefixes for discoverability\n- Design tools around natural workflows, not just API structure\n\n**Use Evaluation-Driven Development:**\n- Create realistic evaluation scenarios early\n- Let agent feedback drive tool improvements\n- Prototype quickly and iterate based on actual agent performance\n\n#### 1.3 Study MCP Protocol Documentation\n\n**Fetch the latest MCP protocol documentation:**\n\nUse WebFetch to load: `https:\u002F\u002Fmodelcontextprotocol.io\u002Fllms-full.txt`\n\nThis comprehensive document contains the complete MCP specification and guidelines.\n\n#### 1.4 Study Framework Documentation\n\n**Load and read the following reference files:**\n\n- **MCP Best Practices**: [? View Best Practices](.\u002Freference\u002Fmcp_best_practices.md) - Core guidelines for all MCP servers\n\n**For Python implementations, also load:**\n- **Python SDK Documentation**: Use WebFetch to load `https:\u002F\u002Fraw.githubusercontent.com\u002Fmodelcontextprotocol\u002Fpython-sdk\u002Fmain\u002FREADME.md`\n- [? Python Implementation Guide](.\u002Freference\u002Fpython_mcp_server.md) - Python-specific best practices and examples\n\n**For Node\u002FTypeScript implementations, also load:**\n- **TypeScript SDK Documentation**: Use WebFetch to load `https:\u002F\u002Fraw.githubusercontent.com\u002Fmodelcontextprotocol\u002Ftypescript-sdk\u002Fmain\u002FREADME.md`\n- [⚡ TypeScript Implementation Guide](.\u002Freference\u002Fnode_mcp_server.md) - Node\u002FTypeScript-specific best practices and examples\n\n#### 1.5 Exhaustively Study API Documentation\n\nTo integrate a service, read through **ALL** available API documentation:\n- Official API reference documentation\n- Authentication and authorization requirements\n- Rate limiting and pagination patterns\n- Error responses and status codes\n- Available endpoints and their parameters\n- Data models and schemas\n\n**To gather comprehensive information, use web search and the WebFetch tool as needed.**\n\n#### 1.6 Create a Comprehensive Implementation Plan\n\nBased on your research, create a detailed plan that includes:\n\n**Tool Selection:**\n- List the most valuable endpoints\u002Foperations to implement\n- Prioritize tools that enable the most common and important use cases\n- Consider which tools work together to enable complex workflows\n\n**Shared Utilities and Helpers:**\n- Identify common API request patterns\n- Plan pagination helpers\n- Design filtering and formatting utilities\n- Plan error handling strategies\n\n**Input\u002FOutput Design:**\n- Define input validation models (Pydantic for Python, Zod for TypeScript)\n- Design consistent response formats (e.g., JSON or Markdown), and configurable levels of detail (e.g., Detailed or Concise)\n- Plan for large-scale usage (thousands of users\u002Fresources)\n- Implement character limits and truncation strategies (e.g., 25,000 tokens)\n\n**Error Handling Strategy:**\n- Plan graceful failure modes\n- Design clear, actionable, LLM-friendly, natural language error messages which prompt further action\n- Consider rate limiting and timeout scenarios\n- Handle authentication and authorization errors\n\n---\n\n### Phase 2: Implementation\n\nNow that you have a comprehensive plan, begin implementation following language-specific best practices.\n\n#### 2.1 Set Up Project Structure\n\n**For Python:**\n- Create a single `.py` file or organize into modules if complex (see [? Python Guide](.\u002Freference\u002Fpython_mcp_server.md))\n- Use the MCP Python SDK for tool registration\n- Define Pydantic models for input validation\n\n**For Node\u002FTypeScript:**\n- Create proper project structure (see [⚡ TypeScript Guide](.\u002Freference\u002Fnode_mcp_server.md))\n- Set up `package.json` and `tsconfig.json`\n- Use MCP TypeScript SDK\n- Define Zod schemas for input validation\n\n#### 2.2 Implement Core Infrastructure First\n\n**To begin implementation, create shared utilities before implementing tools:**\n- API request helper functions\n- Error handling utilities\n- Response formatting functions (JSON and Markdown)\n- Pagination helpers\n- Authentication\u002Ftoken management\n\n#### 2.3 Implement Tools Systematically\n\nFor each tool in the plan:\n\n**Define Input Schema:**\n- Use Pydantic (Python) or Zod (TypeScript) for validation\n- Include proper constraints (min\u002Fmax length, regex patterns, min\u002Fmax values, ranges)\n- Provide clear, descriptive field descriptions\n- Include diverse examples in field descriptions\n\n**Write Comprehensive Docstrings\u002FDescriptions:**\n- One-line summary of what the tool does\n- Detailed explanation of purpose and functionality\n- Explicit parameter types with examples\n- Complete return type schema\n- Usage examples (when to use, when not to use)\n- Error handling documentation, which outlines how to proceed given specific errors\n\n**Implement Tool Logic:**\n- Use shared utilities to avoid code duplication\n- Follow async\u002Fawait patterns for all I\u002FO\n- Implement proper error handling\n- Support multiple response formats (JSON and Markdown)\n- Respect pagination parameters\n- Check character limits and truncate appropriately\n\n**Add Tool Annotations:**\n- `readOnlyHint`: true (for read-only operations)\n- `destructiveHint`: false (for non-destructive operations)\n- `idempotentHint`: true (if repeated calls have same effect)\n- `openWorldHint`: true (if interacting with external systems)\n\n#### 2.4 Follow Language-Specific Best Practices\n\n**At this point, load the appropriate language guide:**\n\n**For Python: Load [? Python Implementation Guide](.\u002Freference\u002Fpython_mcp_server.md) and ensure the following:**\n- Using MCP Python SDK with proper tool registration\n- Pydantic v2 models with `model_config`\n- Type hints throughout\n- Async\u002Fawait for all I\u002FO operations\n- Proper imports organization\n- Module-level constants (CHARACTER_LIMIT, API_BASE_URL)\n\n**For Node\u002FTypeScript: Load [⚡ TypeScript Implementation Guide](.\u002Freference\u002Fnode_mcp_server.md) and ensure the following:**\n- Using `server.registerTool` properly\n- Zod schemas with `.strict()`\n- TypeScript strict mode enabled\n- No `any` types - use proper types\n- Explicit Promise\u003CT> return types\n- Build process configured (`npm run build`)\n\n---\n\n### Phase 3: Review and Refine\n\nAfter initial implementation:\n\n#### 3.1 Code Quality Review\n\nTo ensure quality, review the code for:\n- **DRY Principle**: No duplicated code between tools\n- **Composability**: Shared logic extracted into functions\n- **Consistency**: Similar operations return similar formats\n- **Error Handling**: All external calls have error handling\n- **Type Safety**: Full type coverage (Python type hints, TypeScript types)\n- **Documentation**: Every tool has comprehensive docstrings\u002Fdescriptions\n\n#### 3.2 Test and Build\n\n**Important:** MCP servers are long-running processes that wait for requests over stdio\u002Fstdin or sse\u002Fhttp. Running them directly in your main process (e.g., `python server.py` or `node dist\u002Findex.js`) will cause your process to hang indefinitely.\n\n**Safe ways to test the server:**\n- Use the evaluation harness (see Phase 4) - recommended approach\n- Run the server in tmux to keep it outside your main process\n- Use a timeout when testing: `timeout 5s python server.py`\n\n**For Python:**\n- Verify Python syntax: `python -m py_compile your_server.py`\n- Check imports work correctly by reviewing the file\n- To manually test: Run server in tmux, then test with evaluation harness in main process\n- Or use the evaluation harness directly (it manages the server for stdio transport)\n\n**For Node\u002FTypeScript:**\n- Run `npm run build` and ensure it completes without errors\n- Verify dist\u002Findex.js is created\n- To manually test: Run server in tmux, then test with evaluation harness in main process\n- Or use the evaluation harness directly (it manages the server for stdio transport)\n\n#### 3.3 Use Quality Checklist\n\nTo verify implementation quality, load the appropriate checklist from the language-specific guide:\n- Python: see \"Quality Checklist\" in [? Python Guide](.\u002Freference\u002Fpython_mcp_server.md)\n- Node\u002FTypeScript: see \"Quality Checklist\" in [⚡ TypeScript Guide](.\u002Freference\u002Fnode_mcp_server.md)\n\n---\n\n### Phase 4: Create Evaluations\n\nAfter implementing your MCP server, create comprehensive evaluations to test its effectiveness.\n\n**Load [✅ Evaluation Guide](.\u002Freference\u002Fevaluation.md) for complete evaluation guidelines.**\n\n#### 4.1 Understand Evaluation Purpose\n\nEvaluations test whether LLMs can effectively use your MCP server to answer realistic, complex questions.\n\n#### 4.2 Create 10 Evaluation Questions\n\nTo create effective evaluations, follow the process outlined in the evaluation guide:\n\n1. **Tool Inspection**: List available tools and understand their capabilities\n2. **Content Exploration**: Use READ-ONLY operations to explore available data\n3. **Question Generation**: Create 10 complex, realistic questions\n4. **Answer Verification**: Solve each question yourself to verify answers\n\n#### 4.3 Evaluation Requirements\n\nEach question must be:\n- **Independent**: Not dependent on other questions\n- **Read-only**: Only non-destructive operations required\n- **Complex**: Requiring multiple tool calls and deep exploration\n- **Realistic**: Based on real use cases humans would care about\n- **Verifiable**: Single, clear answer that can be verified by string comparison\n- **Stable**: Answer won't change over time\n\n#### 4.4 Output Format\n\nCreate an XML file with this structure:\n\n```xml\n\u003Cevaluation>\n  \u003Cqa_pair>\n    \u003Cquestion>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?\u003C\u002Fquestion>\n    \u003Canswer>3\u003C\u002Fanswer>\n  \u003C\u002Fqa_pair>\n\u003C!-- More qa_pairs... -->\n\u003C\u002Fevaluation>\n```\n\n---\n\n# Reference Files\n\n## ? Documentation Library\n\nLoad these resources as needed during development:\n\n### Core MCP Documentation (Load First)\n- **MCP Protocol**: Fetch from `https:\u002F\u002Fmodelcontextprotocol.io\u002Fllms-full.txt` - Complete MCP specification\n- [? MCP Best Practices](.\u002Freference\u002Fmcp_best_practices.md) - Universal MCP guidelines including:\n  - Server and tool naming conventions\n  - Response format guidelines (JSON vs Markdown)\n  - Pagination best practices\n  - Character limits and truncation strategies\n  - Tool development guidelines\n  - Security and error handling standards\n\n### SDK Documentation (Load During Phase 1\u002F2)\n- **Python SDK**: Fetch from `https:\u002F\u002Fraw.githubusercontent.com\u002Fmodelcontextprotocol\u002Fpython-sdk\u002Fmain\u002FREADME.md`\n- **TypeScript SDK**: Fetch from `https:\u002F\u002Fraw.githubusercontent.com\u002Fmodelcontextprotocol\u002Ftypescript-sdk\u002Fmain\u002FREADME.md`\n\n### Language-Specific Implementation Guides (Load During Phase 2)\n- [? Python Implementation Guide](.\u002Freference\u002Fpython_mcp_server.md) - Complete Python\u002FFastMCP guide with:\n  - Server initialization patterns\n  - Pydantic model examples\n  - Tool registration with `@mcp.tool`\n  - Complete working examples\n  - Quality checklist\n\n- [⚡ TypeScript Implementation Guide](.\u002Freference\u002Fnode_mcp_server.md) - Complete TypeScript guide with:\n  - Project structure\n  - Zod schema patterns\n  - Tool registration with `server.registerTool`\n  - Complete working examples\n  - Quality checklist\n\n### Evaluation Guide (Load During Phase 4)\n- [✅ Evaluation Guide](.\u002Freference\u002Fevaluation.md) - Complete evaluation creation guide with:\n  - Question creation guidelines\n  - Answer verification strategies\n  - XML format specifications\n  - Example questions and answers\n  - Running an evaluation with the provided scripts\n","# MCP Server Development Guide\n\n## Overview\n\nTo create high-quality MCP (Model Context Protocol) servers that enable LLMs to effectively interact with external services, use this skill. An MCP server provides tools that allow LLMs to access external services and APIs. The quality of an MCP server is measured by how well it enables LLMs to accomplish real-world tasks using the tools provided.\n\n---\n\n# Process\n\n## ? High-Level Workflow\n\nCreating a high-quality MCP server involves four main phases:\n\n### Phase 1: Deep Research and Planning\n\n#### 1.1 Understand Agent-Centric Design Principles\n\nBefore diving into implementation, understand how to design tools for AI agents by reviewing these principles:\n\n**Build for Workflows, Not Just API Endpoints:**\n- Don't simply wrap existing API endpoints - build thoughtful, high-impact workflow tools\n- Consolidate related operations (e.g., `schedule_event` that both checks availability and creates event)\n- Focus on tools that enable complete tasks, not just individual API calls\n- Consider what workflows agents actually need to accomplish\n\n**Optimize for Limited Context:**\n- Agents have constrained context windows - make every token count\n- Return high-signal information, not exhaustive data dumps\n- Provide \"concise\" vs \"detailed\" response format options\n- Default to human-readable identifiers over technical codes (names over IDs)\n- Consider the agent's context budget as a scarce resource\n\n**Design Actionable Error Messages:**\n- Error messages should guide agents toward correct usage patterns\n- Suggest specific next steps: \"Try using filter='active_only' to reduce results\"\n- Make errors educational, not just diagnostic\n- Help agents learn proper tool usage through clear feedback\n\n**Follow Natural Task Subdivisions:**\n- Tool names should reflect how humans think about tasks\n- Group related tools with consistent prefixes for discoverability\n- Design tools around natural workflows, not just API structure\n\n**Use Evaluation-Driven Development:**\n- Create realistic evaluation scenarios early\n- Let agent feedback drive tool improvements\n- Prototype quickly and iterate based on actual agent performance\n\n#### 1.3 Study MCP Protocol Documentation\n\n**Fetch the latest MCP protocol documentation:**\n\nUse WebFetch to load: `https:\u002F\u002Fmodelcontextprotocol.io\u002Fllms-full.txt`\n\nThis comprehensive document contains the complete MCP specification and guidelines.\n\n#### 1.4 Study Framework Documentation\n\n**Load and read the following reference files:**\n\n- **MCP Best Practices**: [? View Best Practices](.\u002Freference\u002Fmcp_best_practices.md) - Core guidelines for all MCP servers\n\n**For Python implementations, also load:**\n- **Python SDK Documentation**: Use WebFetch to load `https:\u002F\u002Fraw.githubusercontent.com\u002Fmodelcontextprotocol\u002Fpython-sdk\u002Fmain\u002FREADME.md`\n- [? Python Implementation Guide](.\u002Freference\u002Fpython_mcp_server.md) - Python-specific best practices and examples\n\n**For Node\u002FTypeScript implementations, also load:**\n- **TypeScript SDK Documentation**: Use WebFetch to load `https:\u002F\u002Fraw.githubusercontent.com\u002Fmodelcontextprotocol\u002Ftypescript-sdk\u002Fmain\u002FREADME.md`\n- [⚡ TypeScript Implementation Guide](.\u002Freference\u002Fnode_mcp_server.md) - Node\u002FTypeScript-specific best practices and examples\n\n#### 1.5 Exhaustively Study API Documentation\n\nTo integrate a service, read through **ALL** available API documentation:\n- Official API reference documentation\n- Authentication and authorization requirements\n- Rate limiting and pagination patterns\n- Error responses and status codes\n- Available endpoints and their parameters\n- Data models and schemas\n\n**To gather comprehensive information, use web search and the WebFetch tool as needed.**\n\n#### 1.6 Create a Comprehensive Implementation Plan\n\nBased on your research, create a detailed plan that includes:\n\n**Tool Selection:**\n- List the most valuable endpoints\u002Foperations to implement\n- Prioritize tools that enable the most common and important use cases\n- Consider which tools work together to enable complex workflows\n\n**Shared Utilities and Helpers:**\n- Identify common API request patterns\n- Plan pagination helpers\n- Design filtering and formatting utilities\n- Plan error handling strategies\n\n**Input\u002FOutput Design:**\n- Define input validation models (Pydantic for Python, Zod for TypeScript)\n- Design consistent response formats (e.g., JSON or Markdown), and configurable levels of detail (e.g., Detailed or Concise)\n- Plan for large-scale usage (thousands of users\u002Fresources)\n- Implement character limits and truncation strategies (e.g., 25,000 tokens)\n\n**Error Handling Strategy:**\n- Plan graceful failure modes\n- Design clear, actionable, LLM-friendly, natural language error messages which prompt further action\n- Consider rate limiting and timeout scenarios\n- Handle authentication and authorization errors\n\n---\n\n### Phase 2: Implementation\n\nNow that you have a comprehensive plan, begin implementation following language-specific best practices.\n\n#### 2.1 Set Up Project Structure\n\n**For Python:**\n- Create a single `.py` file or organize into modules if complex (see [? Python Guide](.\u002Freference\u002Fpython_mcp_server.md))\n- Use the MCP Python SDK for tool registration\n- Define Pydantic models for input validation\n\n**For Node\u002FTypeScript:**\n- Create proper project structure (see [⚡ TypeScript Guide](.\u002Freference\u002Fnode_mcp_server.md))\n- Set up `package.json` and `tsconfig.json`\n- Use MCP TypeScript SDK\n- Define Zod schemas for input validation\n\n#### 2.2 Implement Core Infrastructure First\n\n**To begin implementation, create shared utilities before implementing tools:**\n- API request helper functions\n- Error handling utilities\n- Response formatting functions (JSON and Markdown)\n- Pagination helpers\n- Authentication\u002Ftoken management\n\n#### 2.3 Implement Tools Systematically\n\nFor each tool in the plan:\n\n**Define Input Schema:**\n- Use Pydantic (Python) or Zod (TypeScript) for validation\n- Include proper constraints (min\u002Fmax length, regex patterns, min\u002Fmax values, ranges)\n- Provide clear, descriptive field descriptions\n- Include diverse examples in field descriptions\n\n**Write Comprehensive Docstrings\u002FDescriptions:**\n- One-line summary of what the tool does\n- Detailed explanation of purpose and functionality\n- Explicit parameter types with examples\n- Complete return type schema\n- Usage examples (when to use, when not to use)\n- Error handling documentation, which outlines how to proceed given specific errors\n\n**Implement Tool Logic:**\n- Use shared utilities to avoid code duplication\n- Follow async\u002Fawait patterns for all I\u002FO\n- Implement proper error handling\n- Support multiple response formats (JSON and Markdown)\n- Respect pagination parameters\n- Check character limits and truncate appropriately\n\n**Add Tool Annotations:**\n- `readOnlyHint`: true (for read-only operations)\n- `destructiveHint`: false (for non-destructive operations)\n- `idempotentHint`: true (if repeated calls have same effect)\n- `openWorldHint`: true (if interacting with external systems)\n\n#### 2.4 Follow Language-Specific Best Practices\n\n**At this point, load the appropriate language guide:**\n\n**For Python: Load [? Python Implementation Guide](.\u002Freference\u002Fpython_mcp_server.md) and ensure the following:**\n- Using MCP Python SDK with proper tool registration\n- Pydantic v2 models with `model_config`\n- Type hints throughout\n- Async\u002Fawait for all I\u002FO operations\n- Proper imports organization\n- Module-level constants (CHARACTER_LIMIT, API_BASE_URL)\n\n**For Node\u002FTypeScript: Load [⚡ TypeScript Implementation Guide](.\u002Freference\u002Fnode_mcp_server.md) and ensure the following:**\n- Using `server.registerTool` properly\n- Zod schemas with `.strict()`\n- TypeScript strict mode enabled\n- No `any` types - use proper types\n- Explicit Promise\u003CT> return types\n- Build process configured (`npm run build`)\n\n---\n\n### Phase 3: Review and Refine\n\nAfter initial implementation:\n\n#### 3.1 Code Quality Review\n\nTo ensure quality, review the code for:\n- **DRY Principle**: No duplicated code between tools\n- **Composability**: Shared logic extracted into functions\n- **Consistency**: Similar operations return similar formats\n- **Error Handling**: All external calls have error handling\n- **Type Safety**: Full type coverage (Python type hints, TypeScript types)\n- **Documentation**: Every tool has comprehensive docstrings\u002Fdescriptions\n\n#### 3.2 Test and Build\n\n**Important:** MCP servers are long-running processes that wait for requests over stdio\u002Fstdin or sse\u002Fhttp. Running them directly in your main process (e.g., `python server.py` or `node dist\u002Findex.js`) will cause your process to hang indefinitely.\n\n**Safe ways to test the server:**\n- Use the evaluation harness (see Phase 4) - recommended approach\n- Run the server in tmux to keep it outside your main process\n- Use a timeout when testing: `timeout 5s python server.py`\n\n**For Python:**\n- Verify Python syntax: `python -m py_compile your_server.py`\n- Check imports work correctly by reviewing the file\n- To manually test: Run server in tmux, then test with evaluation harness in main process\n- Or use the evaluation harness directly (it manages the server for stdio transport)\n\n**For Node\u002FTypeScript:**\n- Run `npm run build` and ensure it completes without errors\n- Verify dist\u002Findex.js is created\n- To manually test: Run server in tmux, then test with evaluation harness in main process\n- Or use the evaluation harness directly (it manages the server for stdio transport)\n\n#### 3.3 Use Quality Checklist\n\nTo verify implementation quality, load the appropriate checklist from the language-specific guide:\n- Python: see \"Quality Checklist\" in [? Python Guide](.\u002Freference\u002Fpython_mcp_server.md)\n- Node\u002FTypeScript: see \"Quality Checklist\" in [⚡ TypeScript Guide](.\u002Freference\u002Fnode_mcp_server.md)\n\n---\n\n### Phase 4: Create Evaluations\n\nAfter implementing your MCP server, create comprehensive evaluations to test its effectiveness.\n\n**Load [✅ Evaluation Guide](.\u002Freference\u002Fevaluation.md) for complete evaluation guidelines.**\n\n#### 4.1 Understand Evaluation Purpose\n\nEvaluations test whether LLMs can effectively use your MCP server to answer realistic, complex questions.\n\n#### 4.2 Create 10 Evaluation Questions\n\nTo create effective evaluations, follow the process outlined in the evaluation guide:\n\n1. **Tool Inspection**: List available tools and understand their capabilities\n2. **Content Exploration**: Use READ-ONLY operations to explore available data\n3. **Question Generation**: Create 10 complex, realistic questions\n4. **Answer Verification**: Solve each question yourself to verify answers\n\n#### 4.3 Evaluation Requirements\n\nEach question must be:\n- **Independent**: Not dependent on other questions\n- **Read-only**: Only non-destructive operations required\n- **Complex**: Requiring multiple tool calls and deep exploration\n- **Realistic**: Based on real use cases humans would care about\n- **Verifiable**: Single, clear answer that can be verified by string comparison\n- **Stable**: Answer won't change over time\n\n#### 4.4 Output Format\n\nCreate an XML file with this structure:\n\n```xml\n\u003Cevaluation>\n  \u003Cqa_pair>\n    \u003Cquestion>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?\u003C\u002Fquestion>\n    \u003Canswer>3\u003C\u002Fanswer>\n  \u003C\u002Fqa_pair>\n\u003C!-- More qa_pairs... -->\n\u003C\u002Fevaluation>\n```\n\n---\n\n# Reference Files\n\n## ? Documentation Library\n\nLoad these resources as needed during development:\n\n### Core MCP Documentation (Load First)\n- **MCP Protocol**: Fetch from `https:\u002F\u002Fmodelcontextprotocol.io\u002Fllms-full.txt` - Complete MCP specification\n- [? MCP Best Practices](.\u002Freference\u002Fmcp_best_practices.md) - Universal MCP guidelines including:\n  - Server and tool naming conventions\n  - Response format guidelines (JSON vs Markdown)\n  - Pagination best practices\n  - Character limits and truncation strategies\n  - Tool development guidelines\n  - Security and error handling standards\n\n### SDK Documentation (Load During Phase 1\u002F2)\n- **Python SDK**: Fetch from `https:\u002F\u002Fraw.githubusercontent.com\u002Fmodelcontextprotocol\u002Fpython-sdk\u002Fmain\u002FREADME.md`\n- **TypeScript SDK**: Fetch from `https:\u002F\u002Fraw.githubusercontent.com\u002Fmodelcontextprotocol\u002Ftypescript-sdk\u002Fmain\u002FREADME.md`\n\n### Language-Specific Implementation Guides (Load During Phase 2)\n- [? Python Implementation Guide](.\u002Freference\u002Fpython_mcp_server.md) - Complete Python\u002FFastMCP guide with:\n  - Server initialization patterns\n  - Pydantic model examples\n  - Tool registration with `@mcp.tool`\n  - Complete working examples\n  - Quality checklist\n\n- [⚡ TypeScript Implementation Guide](.\u002Freference\u002Fnode_mcp_server.md) - Complete TypeScript guide with:\n  - Project structure\n  - Zod schema patterns\n  - Tool registration with `server.registerTool`\n  - Complete working examples\n  - Quality checklist\n\n### Evaluation Guide (Load During Phase 4)\n- [✅ Evaluation Guide](.\u002Freference\u002Fevaluation.md) - Complete evaluation creation guide with:\n  - Question creation guidelines\n  - Answer verification strategies\n  - XML format specifications\n  - Example questions and answers\n  - Running an evaluation with the provided scripts\n",{"name":5,"license":50,"description":7},"Complete terms in LICENSE.txt","main","4aa39efd1cdab0b4657ecee16c92a92011a5b10f","awesome-claude-skills\u002Fmcp-builder","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub","https:\u002F\u002Fgithub.com\u002Ffadrienne","2026-10-28 00:43:09",{"source":8,"skillName":5,"commands":58,"prompt":68,"zipAvailable":69},[59,62,65],{"tool":60,"command":61},"npx","npx skills add fadrienne\u002FMyGitHub --skill mcp-builder",{"tool":63,"command":64},"bunx","bunx skills add fadrienne\u002FMyGitHub --skill mcp-builder",{"tool":66,"command":67},"pnpm","pnpm dlx skills add fadrienne\u002FMyGitHub --skill mcp-builder","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fawesome-claude-skills\u002Fmcp-builder\u002FSKILL.md",true,[71,76,82,86,90,94,100,104,108],{"path":72,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":75,"sourceHtmlUrl":16},"LICENSE.txt","LICENSE","许可证",11357,{"path":77,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":80,"sourceHtmlUrl":81},"reference\u002Fevaluation.md","REFERENCE","参考文档",21663,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fawesome-claude-skills\u002Fmcp-builder\u002Freference\u002Fevaluation.md",{"path":83,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":84,"sourceHtmlUrl":85},"reference\u002Fmcp_best_practices.md",28910,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fawesome-claude-skills\u002Fmcp-builder\u002Freference\u002Fmcp_best_practices.md",{"path":87,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":88,"sourceHtmlUrl":89},"reference\u002Fnode_mcp_server.md",26709,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fawesome-claude-skills\u002Fmcp-builder\u002Freference\u002Fnode_mcp_server.md",{"path":91,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":92,"sourceHtmlUrl":93},"reference\u002Fpython_mcp_server.md",26182,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fawesome-claude-skills\u002Fmcp-builder\u002Freference\u002Fpython_mcp_server.md",{"path":95,"resourceType":96,"resourceTypeDesc":97,"sizeBytes":98,"sourceHtmlUrl":99},"scripts\u002Fconnections.py","SCRIPT","脚本",4875,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fawesome-claude-skills\u002Fmcp-builder\u002Fscripts\u002Fconnections.py",{"path":101,"resourceType":96,"resourceTypeDesc":97,"sizeBytes":102,"sourceHtmlUrl":103},"scripts\u002Fevaluation.py",12579,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fawesome-claude-skills\u002Fmcp-builder\u002Fscripts\u002Fevaluation.py",{"path":105,"resourceType":96,"resourceTypeDesc":97,"sizeBytes":106,"sourceHtmlUrl":107},"scripts\u002Fexample_evaluation.xml",1194,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fawesome-claude-skills\u002Fmcp-builder\u002Fscripts\u002Fexample_evaluation.xml",{"path":109,"resourceType":96,"resourceTypeDesc":97,"sizeBytes":110,"sourceHtmlUrl":111},"scripts\u002Frequirements.txt",29,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fawesome-claude-skills\u002Fmcp-builder\u002Fscripts\u002Frequirements.txt",[113,141,165,199,226,253],{"skillNo":114,"name":115,"nameZh":6,"description":116,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":117,"licenseFileUrl":6,"sourceHtmlUrl":118,"categories":119,"keywords":121,"publishedAt":45},"01M7167B28PK20HP7R7ZED2DY0","statistical-power","Sample size calculation, power analysis, and effect size conversion for scientific studies. Use BEFORE data collection (a priori) to determine required N, or after (post-hoc) to compute achieved power. Covers parametric tests (t-test, ANOVA, chi-square, proportions, correlation, regression), simulation-based power for complex\u002Fclustered\u002Fnested designs, and power curves. Use this skill when someone asks 'how many samples do I need', 'what is my power', 'what effect size can I detect', or any variant. Pairs with \u002Fexperimental-design (choose design first, then compute power) and \u002Fstatistical-analysis (analyze data after collection).","MIT","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-power\u002FSKILL.md",[120],{"categoryNo":20,"categoryName":21,"taxonomyType":22,"taxonomyTypeDesc":23},[122,123,124,125,126,127,128,129,28,130,131,132,133,134,135,136,137,138,139,140],"power","statistical power","effect","scipy","regression","choose","design","uv","Bash","collection","analysis","calculation","ANOVA","conversion","analyze","t-test","chi-square","simulation","sample",{"skillNo":142,"name":143,"nameZh":6,"description":144,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":117,"licenseFileUrl":6,"sourceHtmlUrl":145,"categories":146,"keywords":150,"publishedAt":45},"01M71677EBCM8EPCE9BZCK8505","statistical-analysis","Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-analysis\u002FSKILL.md",[147],{"categoryNo":148,"categoryName":149,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000008","测试与质量",[132,151,122,152,153,154,28,155,156,157,29,158,34,30,159,160,161,162,163,164],"statistical analysis","academic research","implementing","selection","you","reporting","data","tests","test","research","results","statsmodels","models","help",{"skillNo":166,"name":167,"nameZh":6,"description":168,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":117,"licenseFileUrl":6,"sourceHtmlUrl":169,"categories":170,"keywords":180,"publishedAt":45},"01M71673R93XAHFH81DBNVNG7H","scientific-writing","Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research-lookup then (2) convert to flowing prose. IMRAD structure, citations (APA\u002FAMA\u002FVancouver), figures\u002Ftables, reporting guidelines (CONSORT\u002FSTROBE\u002FPRISMA), for research papers and journal submissions.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-writing\u002FSKILL.md",[171,174,175],{"categoryNo":172,"categoryName":173,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":20,"categoryName":21,"taxonomyType":22,"taxonomyTypeDesc":23},{"categoryNo":176,"categoryName":177,"taxonomyType":178,"taxonomyTypeDesc":179},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[181,182,183,184,185,186,187,188,189,190,130,191,192,193,194,195,196,197,156,198],"writing","scientific","scientific writing","Citations","deep","deep research","never","papers","journal","false","api","prisma","structure","openrouter","Outlines","core skill","read","features",{"skillNo":200,"name":201,"nameZh":6,"description":202,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":117,"licenseFileUrl":6,"sourceHtmlUrl":203,"categories":204,"keywords":209,"publishedAt":45},"01M7166ZPKXBF4EBPHVTJPQBB1","scientific-visualization","Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib\u002Fseaborn\u002Fplotly with publication styles. For quick exploration use seaborn or plotly directly.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-visualization\u002FSKILL.md",[205,208],{"categoryNo":206,"categoryName":207,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000013","分析与可视化",{"categoryNo":20,"categoryName":21,"taxonomyType":22,"taxonomyTypeDesc":23},[210,182,211,212,213,214,215,216,217,189,28,218,219,220,221,222,223,224,29,225],"visualization","scientific visualization","error","ready","quick","exploration","meta-skill","Meta","safe","plotly","annotations","formatting","Publication","figures","panel","submission",{"skillNo":227,"name":228,"nameZh":6,"description":229,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":117,"licenseFileUrl":6,"sourceHtmlUrl":230,"categories":231,"keywords":238,"publishedAt":45},"01M7166VXJWQSSC3RQZP60WBGR","research-code","Debug, profile, review, and restructure Python research and analysis code. Use when analysis scripts crash, produce wrong results, run too slowly, or need a correctness review. Covers: reading tracebacks and diagnosing root causes, hypothesis-driven debugging, reviewing code for common statistical\u002Fnumerical errors, profiling data pipelines, improving reproducibility (seeds, environments, determinism), and structuring analysis scripts for sharing. Distinct from \u002Fexploratory-data-analysis (which understands data) and \u002Fstatistical-analysis (which chooses and runs tests) — this skill focuses on the code itself.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fresearch-code\u002FSKILL.md",[232,235],{"categoryNo":233,"categoryName":234,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":236,"categoryName":237,"taxonomyType":178,"taxonomyTypeDesc":179},"01JBSEEDCAT000000000000030","开发工程师",[239,240,160,241,242,243,244,129,245,28,130,132,246,247,248,249,250,251,197,252],"code","research code","which","memory","data-analysis","errors","debugging","common","review","data pipelines","run","scripts","line","write",{"skillNo":254,"name":255,"nameZh":6,"description":256,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":257,"categories":258,"keywords":265,"publishedAt":45},"01M7166CF5HSR7SVZYZFWTDX55","markdown-mermaid-writing","Comprehensive markdown and Mermaid diagram writing skill. Use when creating any scientific document, report, analysis, or visualization. Establishes text-based diagrams as the default documentation standard with full style guides (markdown + mermaid), 24 diagram type references, and 9 document templates.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fmarkdown-mermaid-writing\u002FSKILL.md",[259,262,263,264],{"categoryNo":260,"categoryName":261,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":172,"categoryName":173,"taxonomyType":22,"taxonomyTypeDesc":23},{"categoryNo":20,"categoryName":21,"taxonomyType":22,"taxonomyTypeDesc":23},{"categoryNo":176,"categoryName":177,"taxonomyType":178,"taxonomyTypeDesc":179},[181,266,267,268,269,270,210,271,272,28,130,132,273,274,275,276,277,278,182,197],"markdown mermaid writing","mermaid","markdown","community","diagrams","project","default","team","guides","text","documentation","references","diagram"]