mcp-developer skill

by Jeffallan·Jeffallan/claude-skills·12k stars·MIT

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.

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Install the mcp-developer skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description.

git clone --depth 1 https://github.com/Jeffallan/claude-skills.git /tmp/claude-skills
mkdir -p ~/.claude/skills
cp -r /tmp/claude-skills/skills/mcp-developer ~/.claude/skills/mcp-developer

available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

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

Analyze requirements — Identify data sources, tools needed, and client apps
Initialize project — npx @modelcontextprotocol/create-server my-server (TypeScript) or pip install mcp + scaffold (Python)
Design protocol — Define resource URIs, tool schemas (Zod/Pydantic), and prompt templates
Implement — Register tools and resource handlers; configure transport (stdio/SSE/HTTP)
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.
Deploy — Package, add auth/rate-limiting, configure env vars, monitor

Reference Guide

Load detailed guidance based on context:

Minimal Working Example

TypeScript — Tool with Zod Validation

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 schema
server.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 provider
server.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

from mcp.server.fastmcp import FastMCP
from 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:

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

Documentation

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