Anuj Verma

Model Context Protocol: Quick Overview

Model Context Protocol (MCP) is a lightweight way to standardize how models pass contextual data in apps. Think of it as a contract for structuring state or metadata that components can rely on. It helps in managing and propagating contextual data across your application.

It’s particularly useful in scenarios where you need to pass metadata or state (e.g., user info, request IDs) through multiple layers without cluttering function signatures.

It’s dead simple and keeps things predictable.

Here’s a quick example:

interface ModelContext {
  id: string;
  timestamp: number;
  data: Record<string, any>;
}

function processContext(context: ModelContext) {
  console.log(`Processing ${context.id} at ${context.timestamp}`);
  return context.data;
}

const example: ModelContext = {
  id: "user123",
  timestamp: Date.now(),
  data: { name: "Alice", status: "active" }
};

processContext(example);
// Output: "Processing user123 at 1677654321"

That’s it. Define the shape, enforce it with types, and use it. No fluff, just code that works. MCP shines when you need consistency across models without overcomplicating things.

Integrating Model Context Protocol with LLMs

To hook up Model Context Protocol (MCP) with LLMs, treat the MCP as a structured input/output layer. LLMs thrive on clear prompts and consistent data, so MCP’s rigid shape keeps things tight. You’ll typically serialize the context, feed it to the LLM, and parse the response back into MCP.

Here’s a example using a LLM APIs:

import OpenAI from "openai";

interface ModelContext {
  id: string;
  timestamp: number;
  data: Record<string, any>;
}

// Initialize ChatGPT API
const openai = new OpenAI({
  apiKey: "your-api-key-here", // Replace with your OpenAI key
});

// Process MCP with ChatGPT
async function processWithChatGPT(context: ModelContext): Promise<ModelContext> {
  const prompt = `Given this context: ${JSON.stringify(context)}, update the data field and return it as JSON.`;
  
  const response = await openai.chat.completions.create({
    model: "gpt-4", // or "gpt-3.5-turbo"
    messages: [
      { role: "system", content: "You are a helpful assistant that processes JSON data." },
      { role: "user", content: prompt },
    ],
    max_tokens: 200,
  });

  const llmOutput = response.choices[0].message.content;
  const updatedData = JSON.parse(llmOutput); // Expect ChatGPT to return valid JSON

  return {
    ...context,
    data: updatedData,
  };
}

// Usage
const input: ModelContext = {
  id: "user456",
  timestamp: Date.now(),
  data: { task: "Summarize: AI is cool and helps coders." },
};

processWithChatGPT(input)
  .then((result) => console.log("Updated context:", result))
  .catch((err) => console.error("Error:", err));

Heads-up:

ChatGPT might not always return perfect JSON—add validation or error handling.
Costs apply; monitor your API usage.

That’s it. MCP + ChatGPT = structured LLM integration, no mess.