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    "sections": [
      {
        "title": "Overview",
        "body": "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."
      },
      {
        "title": "🚀 High-Level Workflow",
        "body": "Creating a high-quality MCP server involves four main phases:"
      },
      {
        "title": "Phase 1: Deep Research and Planning",
        "body": "1.1 Understand Modern MCP Design\n\nAPI Coverage vs. Workflow Tools:\nBalance 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.\n\nTool Naming and Discoverability:\nClear, 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.\n\nContext Management:\nAgents 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.\n\nActionable Error Messages:\nError messages should guide agents toward solutions with specific suggestions and next steps.\n\n1.2 Study MCP Protocol Documentation\n\nNavigate the MCP specification:\n\nStart with the sitemap to find relevant pages: https://modelcontextprotocol.io/sitemap.xml\n\nThen fetch specific pages with .md suffix for markdown format (e.g., https://modelcontextprotocol.io/specification/draft.md).\n\nKey pages to review:\n\nSpecification overview and architecture\nTransport mechanisms (streamable HTTP, stdio)\nTool, resource, and prompt definitions\n\n1.3 Study Framework Documentation\n\nRecommended stack:\n\nLanguage: 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)\nTransport: 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.\n\nLoad framework documentation:\n\nMCP Best Practices: 📋 View Best Practices - Core guidelines\n\nFor TypeScript (recommended):\n\nTypeScript SDK: Use WebFetch to load https://raw.githubusercontent.com/modelcontextprotocol/typescript-sdk/main/README.md\n⚡ TypeScript Guide - TypeScript patterns and examples\n\nFor Python:\n\nPython SDK: Use WebFetch to load https://raw.githubusercontent.com/modelcontextprotocol/python-sdk/main/README.md\n🐍 Python Guide - Python patterns and examples\n\n1.4 Plan Your Implementation\n\nUnderstand the API:\nReview the service's API documentation to identify key endpoints, authentication requirements, and data models. Use web search and WebFetch as needed.\n\nTool Selection:\nPrioritize comprehensive API coverage. List endpoints to implement, starting with the most common operations."
      },
      {
        "title": "Phase 2: Implementation",
        "body": "2.1 Set Up Project Structure\n\nSee language-specific guides for project setup:\n\n⚡ TypeScript Guide - Project structure, package.json, tsconfig.json\n🐍 Python Guide - Module organization, dependencies\n\n2.2 Implement Core Infrastructure\n\nCreate shared utilities:\n\nAPI client with authentication\nError handling helpers\nResponse formatting (JSON/Markdown)\nPagination support\n\n2.3 Implement Tools\n\nFor each tool:\n\nInput Schema:\n\nUse Zod (TypeScript) or Pydantic (Python)\nInclude constraints and clear descriptions\nAdd examples in field descriptions\n\nOutput Schema:\n\nDefine outputSchema where possible for structured data\nUse structuredContent in tool responses (TypeScript SDK feature)\nHelps clients understand and process tool outputs\n\nTool Description:\n\nConcise summary of functionality\nParameter descriptions\nReturn type schema\n\nImplementation:\n\nAsync/await for I/O operations\nProper error handling with actionable messages\nSupport pagination where applicable\nReturn both text content and structured data when using modern SDKs\n\nAnnotations:\n\nreadOnlyHint: true/false\ndestructiveHint: true/false\nidempotentHint: true/false\nopenWorldHint: true/false"
      },
      {
        "title": "Phase 3: Review and Test",
        "body": "3.1 Code Quality\n\nReview for:\n\nNo duplicated code (DRY principle)\nConsistent error handling\nFull type coverage\nClear tool descriptions\n\n3.2 Build and Test\n\nTypeScript:\n\nRun npm run build to verify compilation\nTest with MCP Inspector: npx @modelcontextprotocol/inspector\n\nPython:\n\nVerify syntax: python -m py_compile your_server.py\nTest with MCP Inspector\n\nSee language-specific guides for detailed testing approaches and quality checklists."
      },
      {
        "title": "Phase 4: Create Evaluations",
        "body": "After implementing your MCP server, create comprehensive evaluations to test its effectiveness.\n\nLoad ✅ Evaluation Guide for complete evaluation guidelines.\n\n4.1 Understand Evaluation Purpose\n\nUse evaluations to test whether LLMs can effectively use your MCP server to answer realistic, complex questions.\n\n4.2 Create 10 Evaluation Questions\n\nTo create effective evaluations, follow the process outlined in the evaluation guide:\n\nTool Inspection: List available tools and understand their capabilities\nContent Exploration: Use READ-ONLY operations to explore available data\nQuestion Generation: Create 10 complex, realistic questions\nAnswer Verification: Solve each question yourself to verify answers\n\n4.3 Evaluation Requirements\n\nEnsure each question is:\n\nIndependent: Not dependent on other questions\nRead-only: Only non-destructive operations required\nComplex: Requiring multiple tool calls and deep exploration\nRealistic: Based on real use cases humans would care about\nVerifiable: Single, clear answer that can be verified by string comparison\nStable: Answer won't change over time\n\n4.4 Output Format\n\nCreate an XML file with this structure:\n\n<evaluation>\n  <qa_pair>\n    <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>\n    <answer>3</answer>\n  </qa_pair>\n<!-- More qa_pairs... -->\n</evaluation>"
      },
      {
        "title": "📚 Documentation Library",
        "body": "Load these resources as needed during development:"
      },
      {
        "title": "Core MCP Documentation (Load First)",
        "body": "MCP Protocol: Start with sitemap at https://modelcontextprotocol.io/sitemap.xml, then fetch specific pages with .md suffix\n📋 MCP Best Practices - Universal MCP guidelines including:\n\nServer and tool naming conventions\nResponse format guidelines (JSON vs Markdown)\nPagination best practices\nTransport selection (streamable HTTP vs stdio)\nSecurity and error handling standards"
      },
      {
        "title": "SDK Documentation (Load During Phase 1/2)",
        "body": "Python SDK: Fetch from https://raw.githubusercontent.com/modelcontextprotocol/python-sdk/main/README.md\nTypeScript SDK: Fetch from https://raw.githubusercontent.com/modelcontextprotocol/typescript-sdk/main/README.md"
      },
      {
        "title": "Language-Specific Implementation Guides (Load During Phase 2)",
        "body": "🐍 Python Implementation Guide - Complete Python/FastMCP guide with:\n\nServer initialization patterns\nPydantic model examples\nTool registration with @mcp.tool\nComplete working examples\nQuality checklist\n\n\n\n⚡ TypeScript Implementation Guide - Complete TypeScript guide with:\n\nProject structure\nZod schema patterns\nTool registration with server.registerTool\nComplete working examples\nQuality checklist"
      },
      {
        "title": "Evaluation Guide (Load During Phase 4)",
        "body": "✅ Evaluation Guide - Complete evaluation creation guide with:\n\nQuestion creation guidelines\nAnswer verification strategies\nXML format specifications\nExample questions and answers\nRunning an evaluation with the provided scripts"
      }
    ],
    "body": "MCP Server Development Guide\nOverview\n\nCreate 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.\n\nProcess\n🚀 High-Level Workflow\n\nCreating a high-quality MCP server involves four main phases:\n\nPhase 1: Deep Research and Planning\n1.1 Understand Modern MCP Design\n\nAPI 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.\n\nTool 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.\n\nContext 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.\n\nActionable Error Messages: Error messages should guide agents toward solutions with specific suggestions and next steps.\n\n1.2 Study MCP Protocol Documentation\n\nNavigate the MCP specification:\n\nStart with the sitemap to find relevant pages: https://modelcontextprotocol.io/sitemap.xml\n\nThen fetch specific pages with .md suffix for markdown format (e.g., https://modelcontextprotocol.io/specification/draft.md).\n\nKey pages to review:\n\nSpecification overview and architecture\nTransport mechanisms (streamable HTTP, stdio)\nTool, resource, and prompt definitions\n1.3 Study Framework Documentation\n\nRecommended stack:\n\nLanguage: 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)\nTransport: 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.\n\nLoad framework documentation:\n\nMCP Best Practices: 📋 View Best Practices - Core guidelines\n\nFor TypeScript (recommended):\n\nTypeScript SDK: Use WebFetch to load https://raw.githubusercontent.com/modelcontextprotocol/typescript-sdk/main/README.md\n⚡ TypeScript Guide - TypeScript patterns and examples\n\nFor Python:\n\nPython SDK: Use WebFetch to load https://raw.githubusercontent.com/modelcontextprotocol/python-sdk/main/README.md\n🐍 Python Guide - Python patterns and examples\n1.4 Plan Your Implementation\n\nUnderstand the API: Review the service's API documentation to identify key endpoints, authentication requirements, and data models. Use web search and WebFetch as needed.\n\nTool Selection: Prioritize comprehensive API coverage. List endpoints to implement, starting with the most common operations.\n\nPhase 2: Implementation\n2.1 Set Up Project Structure\n\nSee language-specific guides for project setup:\n\n⚡ TypeScript Guide - Project structure, package.json, tsconfig.json\n🐍 Python Guide - Module organization, dependencies\n2.2 Implement Core Infrastructure\n\nCreate shared utilities:\n\nAPI client with authentication\nError handling helpers\nResponse formatting (JSON/Markdown)\nPagination support\n2.3 Implement Tools\n\nFor each tool:\n\nInput Schema:\n\nUse Zod (TypeScript) or Pydantic (Python)\nInclude constraints and clear descriptions\nAdd examples in field descriptions\n\nOutput Schema:\n\nDefine outputSchema where possible for structured data\nUse structuredContent in tool responses (TypeScript SDK feature)\nHelps clients understand and process tool outputs\n\nTool Description:\n\nConcise summary of functionality\nParameter descriptions\nReturn type schema\n\nImplementation:\n\nAsync/await for I/O operations\nProper error handling with actionable messages\nSupport pagination where applicable\nReturn both text content and structured data when using modern SDKs\n\nAnnotations:\n\nreadOnlyHint: true/false\ndestructiveHint: true/false\nidempotentHint: true/false\nopenWorldHint: true/false\nPhase 3: Review and Test\n3.1 Code Quality\n\nReview for:\n\nNo duplicated code (DRY principle)\nConsistent error handling\nFull type coverage\nClear tool descriptions\n3.2 Build and Test\n\nTypeScript:\n\nRun npm run build to verify compilation\nTest with MCP Inspector: npx @modelcontextprotocol/inspector\n\nPython:\n\nVerify syntax: python -m py_compile your_server.py\nTest with MCP Inspector\n\nSee language-specific guides for detailed testing approaches and quality checklists.\n\nPhase 4: Create Evaluations\n\nAfter implementing your MCP server, create comprehensive evaluations to test its effectiveness.\n\nLoad ✅ Evaluation Guide for complete evaluation guidelines.\n\n4.1 Understand Evaluation Purpose\n\nUse evaluations to test whether LLMs can effectively use your MCP server to answer realistic, complex questions.\n\n4.2 Create 10 Evaluation Questions\n\nTo create effective evaluations, follow the process outlined in the evaluation guide:\n\nTool Inspection: List available tools and understand their capabilities\nContent Exploration: Use READ-ONLY operations to explore available data\nQuestion Generation: Create 10 complex, realistic questions\nAnswer Verification: Solve each question yourself to verify answers\n4.3 Evaluation Requirements\n\nEnsure each question is:\n\nIndependent: Not dependent on other questions\nRead-only: Only non-destructive operations required\nComplex: Requiring multiple tool calls and deep exploration\nRealistic: Based on real use cases humans would care about\nVerifiable: Single, clear answer that can be verified by string comparison\nStable: Answer won't change over time\n4.4 Output Format\n\nCreate an XML file with this structure:\n\n<evaluation>\n  <qa_pair>\n    <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>\n    <answer>3</answer>\n  </qa_pair>\n<!-- More qa_pairs... -->\n</evaluation>\n\nReference Files\n📚 Documentation Library\n\nLoad these resources as needed during development:\n\nCore MCP Documentation (Load First)\nMCP Protocol: Start with sitemap at https://modelcontextprotocol.io/sitemap.xml, then fetch specific pages with .md suffix\n📋 MCP Best Practices - Universal MCP guidelines including:\nServer and tool naming conventions\nResponse format guidelines (JSON vs Markdown)\nPagination best practices\nTransport selection (streamable HTTP vs stdio)\nSecurity and error handling standards\nSDK Documentation (Load During Phase 1/2)\nPython SDK: Fetch from https://raw.githubusercontent.com/modelcontextprotocol/python-sdk/main/README.md\nTypeScript SDK: Fetch from https://raw.githubusercontent.com/modelcontextprotocol/typescript-sdk/main/README.md\nLanguage-Specific Implementation Guides (Load During Phase 2)\n\n🐍 Python Implementation Guide - Complete Python/FastMCP guide with:\n\nServer initialization patterns\nPydantic model examples\nTool registration with @mcp.tool\nComplete working examples\nQuality checklist\n\n⚡ TypeScript Implementation Guide - Complete TypeScript guide with:\n\nProject structure\nZod schema patterns\nTool registration with server.registerTool\nComplete working examples\nQuality checklist\nEvaluation Guide (Load During Phase 4)\n✅ Evaluation Guide - Complete evaluation creation guide with:\nQuestion creation guidelines\nAnswer verification strategies\nXML format specifications\nExample questions and answers\nRunning an evaluation with the provided scripts"
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    "owner": "uniquevme",
    "version": "0.1.0",
    "license": null,
    "verificationStatus": "Indexed source record"
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