LLMs are smart — but most of them are locked out of your product. They can chat, answer questions, and generate text, but when it comes to actually doing something inside your application — like fetching user data, updating dashboards, or triggering a workflow — they’re stuck. Why? Because most apps weren’t designed to communicate with large language models, and LLMs weren’t built to navigate your API jungle.
That’s exactly where Model Context Protocol (MCP) steps in — a new open standard that allows LLMs to talk to your app, safely and meaningfully. Instead of building endless one-to-one integrations, MCP gives your app a way to describe what it can do. The LLM uses that context to perform real actions, not just offer suggestions. It’s like plugging a brain into your product — without rewriting your stack.
ith MCP in place, your LLM becomes more than a conversation tool. It becomes an assistant that understands your users, works inside your UI, and makes things happen — securely and contextually. It doesn’t hallucinate commands, guess endpoints, or need you to expose sensitive APIs to the outside world. It knows what’s available, what’s safe, and what to do. That’s already happening at scale. Take Zerodha, one of India’s leading stock platforms. They integrated MCP to enable an LLM to support users directly inside their portfolio view. Now, users can ask questions like “What’s my P&L on smallcap funds?” or “Should I rebalance my midcap exposure?” and get real-time, accurate responses. The model fetches the right data, understands the context, and offers relevant, actionable insights — all within the Zerodha environment. No external scraping, no guesswork, no extra API load.
So what exactly can LLMs do inside your app using MCP?
- Discover available actions within your system
- Securely fetch and update user data
- Trigger real-time workflows like rebalancing, routing, or reporting
- Avoid hallucinations by understanding available API routes
- Communicate like a user — but act like a developer
