[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6RBYSCNPBERSS33STAD5SQH":3,"skill-same:01M6RBYSCNPBERSS33STAD5SQH":110},{"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":6,"sourceHtmlUrl":16,"categories":17,"keywords":31,"publishedAt":52,"linkOnly":53,"markdownContent":54,"markdownBody":55,"frontMatter":56,"refName":66,"commitSha":67,"skillDirectory":68,"forksCount":69,"repositoryHtmlUrl":70,"repositoryHomepageUrl":6,"ownerHtmlUrl":71,"ownerBlogUrl":72,"ownerDescription":6,"updatedAt":73,"install":74,"resources":87},"01M6RBYSCNPBERSS33STAD5SQH","mcp-developer",null,"Use when building, debugging, or extending MCP servers or clients that connect AI systems with external tools and data sources. Invoke to implement tool handlers, configure resource providers, set up stdio\u002FHTTP\u002FSSE transport layers, validate schemas with Zod or Pydantic, debug protocol compliance issues, or scaffold complete MCP server\u002Fclient projects using TypeScript or Python SDKs.","PIXARTSeu\u002FSynapse","PIXARTSeu","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F274446381?v=4","01M6RB4XPN9YBZCRY4FVN9PJCT","Pixarts ","01M6RB4XTF8G9KHQEMC21FGDKD",12,"MIT","https:\u002F\u002Fgithub.com\u002FPIXARTSeu\u002FSynapse\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fmcp-developer\u002FSKILL.md",[18,23,26],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":27,"categoryName":28,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51],"mcp developer","developer","MCP","zod","data sources","invoke","layers","external tools","expert","engineer","use","context","tool","projects","server","connect","architecture","security","python","typescript","2026-11-10 10:12:30",false,"---\nname: mcp-developer\ndescription: Use when building, debugging, or extending MCP servers or clients that connect AI systems with external tools and data sources. Invoke to implement tool handlers, configure resource providers, set up stdio\u002FHTTP\u002FSSE transport layers, validate schemas with Zod or Pydantic, debug protocol compliance issues, or scaffold complete MCP server\u002Fclient projects using TypeScript or Python SDKs.\nlicense: MIT\nmetadata:\n  author: https:\u002F\u002Fgithub.com\u002FJeffallan\n  version: \"1.1.0\"\n  domain: api-architecture\n  triggers: MCP, Model Context Protocol, MCP server, MCP client, Claude integration, AI tools, context protocol, JSON-RPC\n  role: specialist\n  scope: implementation\n  output-format: code\n  related-skills: fastapi-expert, typescript-pro, security-reviewer, devops-engineer\n---\n\n# MCP Developer\n\nSenior MCP (Model Context Protocol) developer with deep expertise in building servers and clients that connect AI systems with external tools and data sources.\n\n## Core Workflow\n\n1. **Analyze requirements** — Identify data sources, tools needed, and client apps\n2. **Initialize project** — `npx @modelcontextprotocol\u002Fcreate-server my-server` (TypeScript) or `pip install mcp` + scaffold (Python)\n3. **Design protocol** — Define resource URIs, tool schemas (Zod\u002FPydantic), and prompt templates\n4. **Implement** — Register tools and resource handlers; configure transport (stdio\u002FSSE\u002FHTTP)\n5. **Test** — Run `npx @modelcontextprotocol\u002Finspector` to verify protocol compliance interactively; confirm tools appear, schemas accept valid inputs, and error responses are well-formed JSON-RPC 2.0. **Feedback loop:** if schema validation fails → inspect Zod\u002FPydantic error output → fix schema definition → re-run inspector. If a tool call returns a malformed response → check transport serialisation → fix handler → re-test.\n6. **Deploy** — Package, add auth\u002Frate-limiting, configure env vars, monitor\n\n## Reference Guide\n\nLoad detailed guidance based on context:\n\n| Topic | Reference | Load When |\n|-------|-----------|-----------|\n| Protocol | `references\u002Fprotocol.md` | Message types, lifecycle, JSON-RPC 2.0 |\n| TypeScript SDK | `references\u002Ftypescript-sdk.md` | Building servers\u002Fclients in Node.js |\n| Python SDK | `references\u002Fpython-sdk.md` | Building servers\u002Fclients in Python |\n| Tools | `references\u002Ftools.md` | Tool definitions, schemas, execution |\n| Resources | `references\u002Fresources.md` | Resource providers, URIs, templates |\n\n## Minimal Working Example\n\n### TypeScript — Tool with Zod Validation\n\n```typescript\nimport { McpServer } from \"@modelcontextprotocol\u002Fsdk\u002Fserver\u002Fmcp.js\";\nimport { StdioServerTransport } from \"@modelcontextprotocol\u002Fsdk\u002Fserver\u002Fstdio.js\";\nimport { z } from \"zod\";\n\nconst server = new McpServer({ name: \"my-server\", version: \"1.1.0\" });\n\n\u002F\u002F Register a tool with validated input schema\nserver.tool(\n  \"get_weather\",\n  \"Fetch current weather for a location\",\n  {\n    location: z.string().min(1).describe(\"City name or coordinates\"),\n    units: z.enum([\"celsius\", \"fahrenheit\"]).default(\"celsius\"),\n  },\n  async ({ location, units }) => {\n    \u002F\u002F Implementation: call external API, transform response\n    const data = await fetchWeather(location, units); \u002F\u002F your fetch logic\n    return {\n      content: [{ type: \"text\", text: JSON.stringify(data) }],\n    };\n  }\n);\n\n\u002F\u002F Register a resource provider\nserver.resource(\n  \"config:\u002F\u002Fapp\",\n  \"Application configuration\",\n  async (uri) => ({\n    contents: [{ uri: uri.href, text: JSON.stringify(getConfig()), mimeType: \"application\u002Fjson\" }],\n  })\n);\n\nconst transport = new StdioServerTransport();\nawait server.connect(transport);\n```\n\n### Python — Tool with Pydantic Validation\n\n```python\nfrom mcp.server.fastmcp import FastMCP\nfrom pydantic import BaseModel, Field\n\nmcp = FastMCP(\"my-server\")\n\nclass WeatherInput(BaseModel):\n    location: str = Field(..., min_length=1, description=\"City name or coordinates\")\n    units: str = Field(\"celsius\", pattern=\"^(celsius|fahrenheit)$\")\n\n@mcp.tool()\nasync def get_weather(location: str, units: str = \"celsius\") -> str:\n    \"\"\"Fetch current weather for a location.\"\"\"\n    data = await fetch_weather(location, units)  # your fetch logic\n    return str(data)\n\n@mcp.resource(\"config:\u002F\u002Fapp\")\nasync def app_config() -> str:\n    \"\"\"Expose application configuration as a resource.\"\"\"\n    return json.dumps(get_config())\n\nif __name__ == \"__main__\":\n    mcp.run()  # defaults to stdio transport\n```\n\n**Expected tool call flow:**\n```\nClient → { \"method\": \"tools\u002Fcall\", \"params\": { \"name\": \"get_weather\", \"arguments\": { \"location\": \"Berlin\" } } }\nServer → { \"result\": { \"content\": [{ \"type\": \"text\", \"text\": \"{\\\"temp\\\": 18, \\\"units\\\": \\\"celsius\\\"}\" }] } }\n```\n\n## Constraints\n\n### MUST DO\n- Implement JSON-RPC 2.0 protocol correctly\n- Validate all inputs with schemas (Zod\u002FPydantic)\n- Use proper transport mechanisms (stdio\u002FHTTP\u002FSSE)\n- Implement comprehensive error handling\n- Add authentication and authorization\n- Log protocol messages for debugging\n- Test protocol compliance thoroughly\n- Document server capabilities\n\n### MUST NOT DO\n- Skip input validation on tool inputs\n- Expose sensitive data in resource content\n- Ignore protocol version compatibility\n- Mix synchronous code with async transports\n- Hardcode credentials or secrets\n- Return unstructured errors to clients\n- Deploy without rate limiting\n- Skip security controls\n\n## Output Templates\n\nWhen implementing MCP features, provide:\n1. Server\u002Fclient implementation file\n2. Schema definitions (tools, resources, prompts)\n3. Configuration file (transport, auth, etc.)\n4. Brief explanation of design decisions\n","# MCP Developer\n\nSenior MCP (Model Context Protocol) developer with deep expertise in building servers and clients that connect AI systems with external tools and data sources.\n\n## Core Workflow\n\n1. **Analyze requirements** — Identify data sources, tools needed, and client apps\n2. **Initialize project** — `npx @modelcontextprotocol\u002Fcreate-server my-server` (TypeScript) or `pip install mcp` + scaffold (Python)\n3. **Design protocol** — Define resource URIs, tool schemas (Zod\u002FPydantic), and prompt templates\n4. **Implement** — Register tools and resource handlers; configure transport (stdio\u002FSSE\u002FHTTP)\n5. **Test** — Run `npx @modelcontextprotocol\u002Finspector` to verify protocol compliance interactively; confirm tools appear, schemas accept valid inputs, and error responses are well-formed JSON-RPC 2.0. **Feedback loop:** if schema validation fails → inspect Zod\u002FPydantic error output → fix schema definition → re-run inspector. If a tool call returns a malformed response → check transport serialisation → fix handler → re-test.\n6. **Deploy** — Package, add auth\u002Frate-limiting, configure env vars, monitor\n\n## Reference Guide\n\nLoad detailed guidance based on context:\n\n| Topic | Reference | Load When |\n|-------|-----------|-----------|\n| Protocol | `references\u002Fprotocol.md` | Message types, lifecycle, JSON-RPC 2.0 |\n| TypeScript SDK | `references\u002Ftypescript-sdk.md` | Building servers\u002Fclients in Node.js |\n| Python SDK | `references\u002Fpython-sdk.md` | Building servers\u002Fclients in Python |\n| Tools | `references\u002Ftools.md` | Tool definitions, schemas, execution |\n| Resources | `references\u002Fresources.md` | Resource providers, URIs, templates |\n\n## Minimal Working Example\n\n### TypeScript — Tool with Zod Validation\n\n```typescript\nimport { McpServer } from \"@modelcontextprotocol\u002Fsdk\u002Fserver\u002Fmcp.js\";\nimport { StdioServerTransport } from \"@modelcontextprotocol\u002Fsdk\u002Fserver\u002Fstdio.js\";\nimport { z } from \"zod\";\n\nconst server = new McpServer({ name: \"my-server\", version: \"1.1.0\" });\n\n\u002F\u002F Register a tool with validated input schema\nserver.tool(\n  \"get_weather\",\n  \"Fetch current weather for a location\",\n  {\n    location: z.string().min(1).describe(\"City name or coordinates\"),\n    units: z.enum([\"celsius\", \"fahrenheit\"]).default(\"celsius\"),\n  },\n  async ({ location, units }) => {\n    \u002F\u002F Implementation: call external API, transform response\n    const data = await fetchWeather(location, units); \u002F\u002F your fetch logic\n    return {\n      content: [{ type: \"text\", text: JSON.stringify(data) }],\n    };\n  }\n);\n\n\u002F\u002F Register a resource provider\nserver.resource(\n  \"config:\u002F\u002Fapp\",\n  \"Application configuration\",\n  async (uri) => ({\n    contents: [{ uri: uri.href, text: JSON.stringify(getConfig()), mimeType: \"application\u002Fjson\" }],\n  })\n);\n\nconst transport = new StdioServerTransport();\nawait server.connect(transport);\n```\n\n### Python — Tool with Pydantic Validation\n\n```python\nfrom mcp.server.fastmcp import FastMCP\nfrom pydantic import BaseModel, Field\n\nmcp = FastMCP(\"my-server\")\n\nclass WeatherInput(BaseModel):\n    location: str = Field(..., min_length=1, description=\"City name or coordinates\")\n    units: str = Field(\"celsius\", pattern=\"^(celsius|fahrenheit)$\")\n\n@mcp.tool()\nasync def get_weather(location: str, units: str = \"celsius\") -> str:\n    \"\"\"Fetch current weather for a location.\"\"\"\n    data = await fetch_weather(location, units)  # your fetch logic\n    return str(data)\n\n@mcp.resource(\"config:\u002F\u002Fapp\")\nasync def app_config() -> str:\n    \"\"\"Expose application configuration as a resource.\"\"\"\n    return json.dumps(get_config())\n\nif __name__ == \"__main__\":\n    mcp.run()  # defaults to stdio transport\n```\n\n**Expected tool call flow:**\n```\nClient → { \"method\": \"tools\u002Fcall\", \"params\": { \"name\": \"get_weather\", \"arguments\": { \"location\": \"Berlin\" } } }\nServer → { \"result\": { \"content\": [{ \"type\": \"text\", \"text\": \"{\\\"temp\\\": 18, \\\"units\\\": \\\"celsius\\\"}\" }] } }\n```\n\n## Constraints\n\n### MUST DO\n- Implement JSON-RPC 2.0 protocol correctly\n- Validate all inputs with schemas (Zod\u002FPydantic)\n- Use proper transport mechanisms (stdio\u002FHTTP\u002FSSE)\n- Implement comprehensive error handling\n- Add authentication and authorization\n- Log protocol messages for debugging\n- Test protocol compliance thoroughly\n- Document server capabilities\n\n### MUST NOT DO\n- Skip input validation on tool inputs\n- Expose sensitive data in resource content\n- Ignore protocol version compatibility\n- Mix synchronous code with async transports\n- Hardcode credentials or secrets\n- Return unstructured errors to clients\n- Deploy without rate limiting\n- Skip security controls\n\n## Output Templates\n\nWhen implementing MCP features, provide:\n1. Server\u002Fclient implementation file\n2. Schema definitions (tools, resources, prompts)\n3. Configuration file (transport, auth, etc.)\n4. 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