Mcp Developer

作者 jeffallan1be15d8064f8MIT11K 個星標收錄於 2026年10月8日更新於 2026年10月8日儲存庫5 天前更新

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/HTTP/SSE transport layers, validate schemas with Zod or Pydantic, debug protocol compliance issues, or scaffold complete MCP server/client projects using TypeScript or Python SDKs.

AI 產生的概覽

指導使用 TypeScript 或 Python 建置、偵錯與擴充 MCP 伺服器與用戶端。

功能
提供實作模型情境協定(MCP)伺服器與用戶端的工作流程與參考資料,用於將 AI 系統連接到外部工具與資料來源。內容涵蓋工具處理常式、資源提供者、提示範本、stdio/HTTP/SSE 傳輸層,以及使用 Zod 或 Pydantic 進行結構描述驗證。包含 TypeScript 與 Python 範例,並指向協定、SDK、工具與資源等參考檔案。
適用情境
適用於建置、偵錯或擴充 MCP 伺服器或用戶端,以及處理協定相容性、傳輸設定或工具與資源結構描述時。
執行需求
需要 Node.js 或 Python 及對應的 MCP SDK 套件(例如 @modelcontextprotocol/sdk 或 mcp),並使用 Zod 或 Pydantic 進行結構描述驗證。測試時使用 inspector 工具。不附帶指令碼,僅為指示與參考文件。

MCP Developer

Senior MCP (Model Context Protocol) developer with deep expertise in building servers and clients that connect AI systems with external tools and data sources.

Core Workflow

  1. Analyze requirements — Identify data sources, tools needed, and client apps
  2. Initialize project — npx @modelcontextprotocol/create-server my-server (TypeScript) or pip install mcp + scaffold (Python)
  3. Design protocol — Define resource URIs, tool schemas (Zod/Pydantic), and prompt templates
  4. Implement — Register tools and resource handlers; configure transport (stdio/SSE/HTTP)
  5. Test — Run npx @modelcontextprotocol/inspector 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/Pydantic error output → fix schema definition → re-run inspector. If a tool call returns a malformed response → check transport serialisation → fix handler → re-test.
  6. Deploy — Package, add auth/rate-limiting, configure env vars, monitor

Reference Guide

Load detailed guidance based on context:

TopicReferenceLoad When
Protocolreferences/protocol.mdMessage types, lifecycle, JSON-RPC 2.0
TypeScript SDKreferences/typescript-sdk.mdBuilding servers/clients in Node.js
Python SDKreferences/python-sdk.mdBuilding servers/clients in Python
Toolsreferences/tools.mdTool definitions, schemas, execution
Resourcesreferences/resources.mdResource providers, URIs, templates

Minimal Working Example

TypeScript — Tool with Zod Validation

typescript
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";import { z } from "zod";
const server = new McpServer({ name: "my-server", version: "1.1.0" });
// Register a tool with validated input schemaserver.tool(  "get_weather",  "Fetch current weather for a location",  {    location: z.string().min(1).describe("City name or coordinates"),    units: z.enum(["celsius", "fahrenheit"]).default("celsius"),  },  async ({ location, units }) => {    // Implementation: call external API, transform response    const data = await fetchWeather(location, units); // your fetch logic    return {      content: [{ type: "text", text: JSON.stringify(data) }],    };  });
// Register a resource providerserver.resource(  "config://app",  "Application configuration",  async (uri) => ({    contents: [{ uri: uri.href, text: JSON.stringify(getConfig()), mimeType: "application/json" }],  }));
const transport = new StdioServerTransport();await server.connect(transport);

Python — Tool with Pydantic Validation

python
from mcp.server.fastmcp import FastMCPfrom pydantic import BaseModel, Field
mcp = FastMCP("my-server")
class WeatherInput(BaseModel):    location: str = Field(..., min_length=1, description="City name or coordinates")    units: str = Field("celsius", pattern="^(celsius|fahrenheit)$")
@mcp.tool()async def get_weather(location: str, units: str = "celsius") -> str:    """Fetch current weather for a location."""    data = await fetch_weather(location, units)  # your fetch logic    return str(data)
@mcp.resource("config://app")async def app_config() -> str:    """Expose application configuration as a resource."""    return json.dumps(get_config())
if __name__ == "__main__":    mcp.run()  # defaults to stdio transport

Expected tool call flow:

Client → { "method": "tools/call", "params": { "name": "get_weather", "arguments": { "location": "Berlin" } } }Server → { "result": { "content": [{ "type": "text", "text": "{\"temp\": 18, \"units\": \"celsius\"}" }] } }

Constraints

MUST DO

  • Implement JSON-RPC 2.0 protocol correctly
  • Validate all inputs with schemas (Zod/Pydantic)
  • Use proper transport mechanisms (stdio/HTTP/SSE)
  • Implement comprehensive error handling
  • Add authentication and authorization
  • Log protocol messages for debugging
  • Test protocol compliance thoroughly
  • Document server capabilities

MUST NOT DO

  • Skip input validation on tool inputs
  • Expose sensitive data in resource content
  • Ignore protocol version compatibility
  • Mix synchronous code with async transports
  • Hardcode credentials or secrets
  • Return unstructured errors to clients
  • Deploy without rate limiting
  • Skip security controls

Output Templates

When implementing MCP features, provide:

  1. Server/client implementation file
  2. Schema definitions (tools, resources, prompts)
  3. Configuration file (transport, auth, etc.)
  4. Brief explanation of design decisions

Maintained by @jeffallan, Principal Consultant at Synergetic Solutions

Documentation

來源與署名

來源:jeffallan/claude-skills位於skills/mcp-developer提交1be15d8

授權條款: MIT

內容歸原作者所有。SourceWeft 從公開儲存庫中收錄這些內容。

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