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Model Context Protocol - Structure for Building AI Agents

The Model Context Protocol (MCP) is a structured framework for designing intelligent systems powered by large language models (LLMs). By splitting an agent into four distinct layers— Model, Context, Tools, and Control Plane—MCP promotes modularity, scalability, and maintainability.

Model Context Protocol (MCP) Overview
Model Context Protocol (MCP) Overview

🧩 Core Components of MCP

Architecture Deep-Dive

The MCP layers interact in a clear pipeline:

Sample Workflow

Example: a user asks for stock prices.

🚀 Benefits of Using MCP

TypeScript Code Example

Here’s a minimal bootstrap showing MCP interfaces and a simple agent loop:


// mcp.ts

/** 1) Define core interfaces **/
export interface ContextManager {
  getHistory(userId: string): Promise<string[]>;
  appendTurn(userId: string, turn: string): Promise<void>;
}

export interface Tool {
  name: string;
  call(args: any): Promise<any>;
}

export interface ControlPlane {
  log(entry: any): void;
  enforce(entry: any): void;
}

// 2) Agent class wiring components
export class MCPAgent {
  constructor(
    private model: (prompt: string) => Promise<string>,
    private context: ContextManager,
    private tools: Record<string, Tool>,
    private control: ControlPlane
  ) {}

  async handle(userId: string, userInput: string) {
    // 2a) Enforce policy
    this.control.enforce({ userId, userInput });

    // 2b) Build prompt
    const history = await this.context.getHistory(userId);
    const prompt = [...history, userInput].join("\n");

    // 2c) Model inference
    const raw = await this.model(prompt);

    // 2d) If tool call detected, route to tool
    if (raw.startsWith("CALL_TOOL:")) {
      const [_, toolName, argJson] = raw.split("|");
      const result = await this.tools[toolName].call(JSON.parse(argJson));
      return this.handle(userId, result);
    }

    // 2e) Log & update memory
    this.control.log({ userId, prompt, response: raw });
    await this.context.appendTurn(userId, userInput);
    await this.context.appendTurn(userId, raw);

    return raw;
  }
}

// 3) Example instantiation omitted for brevity

Where MCP is Used

MCP is commonly adopted in modern AI frameworks such as LangChain, CrewAI, and LangGraph. It’s ideal for production-level agents, copilots, and data-driven chatbots.

Conclusions

The Model Context Protocol (MCP) gives you a rock-solid scaffold for building LLM-powered agents. By clearly separating the Model, Context, Tools, and Control Plane, you gain flexibility to swap implementations, enforce governance, and scale your system. Whether you’re crafting customer support bots, data analysis copilots, or complex task automators, MCP ensures each piece of your agent is maintainable, testable, and production-ready.

Resources

Get started with the Model Context Protocol (MCP)
The Model Context Protocol (MCP) — A Complete Tutorial