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.
npx skills add https://github.com/Jeffallan/claude-skills --skill 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.
npx @modelcontextprotocol/create-server my-server (TypeScript) or pip install mcp + scaffold (Python)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.Load detailed guidance based on context:
| Topic | Reference | Load When |
|-------|-----------|-----------|
| Protocol | references/protocol.md | Message types, lifecycle, JSON-RPC 2.0 |
| TypeScript SDK | references/typescript-sdk.md | Building servers/clients in Node.js |
| Python SDK | references/python-sdk.md | Building servers/clients in Python |
| Tools | references/tools.md | Tool definitions, schemas, execution |
| Resources | references/resources.md | Resource providers, URIs, templates |
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);
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\"}" }] } }
When implementing MCP features, provide:
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/TypeScript (MCP SDK).
Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Turns hours of manual changelog writing into minutes of automated generation.
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
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/TypeScript (MCP SDK).
React Native and Expo best practices for building performant mobile apps. Use when building React Native components, optimizing list performance, implementing animations, or working with native modules. Triggers on tasks involving React Native, Expo, mobile performance, or native platform APIs.
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
Next.js best practices - file conventions, RSC boundaries, data patterns, async APIs, metadata, error handling, route handlers, image/font optimization, bundling
Use when starting feature work that needs isolation from current workspace or before executing implementation plans - creates isolated git worktrees with smart directory selection and safety verification
Take jeffallan/mcp-developer from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference pip, npx.
Without those the skill loads but fails at the first command.