What problem does it solve?
This Skill guides you through building high-quality Model Context Protocol (MCP) servers, enabling LLMs to interact with external services and APIs effectively. It reduces the complexity of tool integration, allowing AI to automate tasks across your entire digital ecosystem.
Core Features & Use Cases
- Agent-Centric Tool Design: Learn to design tools optimized for AI agent workflows, focusing on high-impact, consolidated operations rather than raw API endpoints.
- Comprehensive SDK Guidance: Get detailed instructions and best practices for implementing MCP servers in both Python (FastMCP) and Node/TypeScript (MCP SDK).
- Evaluation-Driven Development: Create robust evaluations to ensure your MCP server tools are effective, reliable, and truly useful for LLMs in real-world scenarios.
- Use Case: You want to integrate your company's internal CRM system with an LLM. Use this Skill to build an MCP server that exposes CRM functionalities as tools, allowing the LLM to manage customer data, create reports, and automate sales tasks, freeing up your team's time.
Quick Start
Example: A simple Python MCP tool
from mcp.server.fastmcp import FastMCP
from pydantic import BaseModel, Field
from typing import Optional
mcp = FastMCP("service_mcp")
class MyToolInput(BaseModel):
message: str = Field(..., description="A message to process")
@mcp.tool(name="process_message", annotations={"readOnlyHint": True})
async def process_message(params: MyToolInput) -> str:
return f"Processed: {params.message}"
if name == "main":
mcp.run()
