Imagine a world where your AI assistant can not only understand your commands but also connect directly to apps like Zerodha for trading stocks. This is now possible thanks to Model Context Protocol (MCP). MCP makes it simple for language models like Claude to talk to outside services securely. This opens doors to automate tasks like trading, managing content, or controlling devices just using natural language.

🤖 What is Model Context Protocol (MCP)?

MCP is an open standard developed by Anthropic that allows AI models to securely and seamlessly communicate with external applications. It acts like a universal translator, letting AIs like Claude or ChatGPT perform actions in apps without needing to understand the internal logic of those apps.

MCP Architecture

🛠️ Why Was MCP Introduced?

Before MCP, every AI-to-app integration required custom logic. This wasn’t scalable. MCP solves:

  • How AI can securely interact with external tools.
  • How developers can expose their app’s functions in a standard way.
  • How to avoid rewriting everything for new integrations.

🌐 MCP Servers and Real-World Use Cases

Here are some existing servers built with MCP that showcase its power:

  • Brave Search: Let AI search the web for facts.
  • GitHub: Review PRs, fetch code.
  • Blender: Control 3D models.
  • YouTube: Manage or upload content.
  • Kubernetes: Start or stop containers.
  • Webflow: Edit and manage websites via chat.

📈 Build an AI-Powered Trading Bot with Zerodha + MCP

Step 1: Set Up Zerodha API 

const KiteConnect = require("kiteconnect").KiteConnect;
const kc = new KiteConnect({ api_key: "your_api_key" });

kc.generateSession("request_token", "api_secret")
  .then(response => {
    kc.setAccessToken(response.access_token);
    return kc.placeOrder("regular", {
      exchange: "NSE",
      tradingsymbol: "TATAMOTORS",
      transaction_type: "BUY",
      quantity: 20,
      product: "CNC",
      order_type: "MARKET"
    });
  })
  .then(order => console.log("Order ID:", order.order_id))
  .catch(console.error);

Step 2: Define MCP Tools for Trading

Expose the actions your bot can perform:

  • buy-stock: Buys a stock.
  • sell-stock: Sells a stock.
  • show-portfolio: Lists holdings.
  • cancel-order: Cancels pending orders.

Each tool needs a schema and a handler.

server.tool.add("buy-stock", {
  description: "Buy a specific stock",
  schema: {
    symbol: "string",
    quantity: "number"
  },
  execute: async ({ symbol, quantity }) => {
    await placeOrder(symbol, quantity, "BUY");
  }
});

Step 3: Set Up Your MCP Server

Using the JavaScript SDK:

const { createServer } = require("mcp-sdk");
const server = createServer();

// Add tools here
server.listen(3000);

🔗 Connect Your Server to Claude or Other LLMs

  • Run the MCP server locally or on the cloud.
  • Register it in Claude or your AI platform’s tool settings.
  • Ensure secure comms between the LLM and your MCP server.

💡 Future Use Cases

MCP isn’t just for trading. You can:

  • Voice-control 3D modeling tools.
  • Automate cloud infrastructure.
  • Build AI-managed content platforms.
  • Let AI create and deploy websites.

🔐 Actionable Tips

  • Always validate input with schemas.
  • Provide tool descriptions for the AI.
  • Secure your API keys and tokens.
  • Log and monitor AI actions for safety.

🧠 Final Thoughts

MCP is the missing puzzle piece in building secure, smart, AI-integrated systems. Whether you want to build a trading bot or automate devops, MCP gives you the tools and structure to do it right.

Want to build your first server? Check out the MCP SDK and Kite Connect Docs to start automating today.