The Role of MCP in Modern AI Development

MCP acts as a universal translator between AI models and tools. When an AI model needs to perform a specific task - whether it's analyzing an image, querying a database, or making an API call - MCP provides the structured format for this interaction. This standardization brings several key benefits to modern AI development.

Simplified Integration

Enhanced Scalability

Key Features

•Tool definitions with clear interfaces
•Parameter validation and type safety
•Error handling and responses
•API endpoint structure

Implementation Example: Weather Tool

// Basic MCP Tool Interface
interface MCPTool {
  name: string;
  description: string;
  parameters: { type: "object"; properties: Record<string, unknown>; required: string[] };
  execute: (params: Record<string, unknown>) => Promise<unknown>;
}

// Example Weather Tool Implementation
const weatherTool: MCPTool = {
  name: "getWeather",
  description: "Get current weather for a location",
  parameters: {
    type: "object",
    properties: {
      location: { type: "string", description: "The city or ZIP code to get weather for" },
    },
    required: ["location"],
  },
  execute: async (params: Record<string, unknown>) => {
    const { location } = params as { location: string };
    const mockWeatherData = {
      location,
      temperature: Math.floor(Math.random() * 30) + 10,
      condition: ["Sunny", "Cloudy", "Rainy"][Math.floor(Math.random() * 3)],
      timestamp: new Date().toISOString(),
    };
    return mockWeatherData;
  },
};

// Example Usage
async function callMCPTool(tool: MCPTool, input: Record<string, unknown>) {
  try { return await tool.execute(input); } catch (error) { throw new Error("Failed to execute MCP tool"); }
}