In the rapidly evolving world of artificial intelligence, the Model Context Protocol (MCP) is a breakthrough that allows language models (like Claude, ChatGPT, etc.) to interact with external data and services in a secure, structured, and real-time manner. Originally proposed by Anthropic, MCP is quickly becoming the standard for enabling AI agents to “see” and “act on” real-world applications—without compromising user control or privacy.
What is Model Context Protocol (MCP)?
MCP is a communication framework designed to allow language models to access live, contextual data by interfacing with external servers via a standardized API protocol. Unlike traditional static prompt-based inputs, MCP gives the model an extended context and actionable data fetched directly from a user’s systems or devices.
In simpler terms:
It lets AI assistants “talk” to your apps, accounts, and tools.
It ensures the AI model receives updated, relevant information during the conversation.
It supports read-only and write-limited interactions in a safe and controlled environment.
What is an MCP Server?
An MCP server acts as the secure bridge between the AI model and external services (e.g., your portfolio data from Zerodha, system logs, user preferences, etc.).
Key Responsibilities of an MCP Server:
Data Mediation: It connects to your internal systems (like databases, APIs, or services) and converts the information into a format that the AI model understands.
Context Injection: Provides dynamic context to the AI assistant via structured JSON, YAML, or other supported formats.
Authorization & Access Control: Only authorized users can interact with the AI using their private data. The server enforces read-only or limited write scopes.
No Model Control: The server doesn’t host the AI model; it simply serves context to the model as requested.
How MCP Works (Workflow)
Here’s a simplified view of the process:
User Initiates Interaction:
Example: A user asks their AI assistant, “What is my current stock portfolio value?”
AI Model Triggers MCP Hook:
The model identifies that it needs external data (e.g., from Zerodha) and sends a request to the MCP server.
MCP Server Processes Request:
Authenticates the user
Fetches real-time data via API (e.g., current stock holdings)
Formats the data (e.g., JSON with key metrics)
Context Response Sent to Model:
The AI receives structured information (like portfolio breakdown, value, gains/losses).
Model Uses Context to Respond:
The assistant now gives a human-friendly reply with analysis based on the data received.
Is MCP Secure?
Yes. Security is a core feature of the protocol:
OAuth 2.0 Authorization: Access to user data requires explicit consent.
Read-only by default: Most implementations limit AI to view-only access.
Isolated Execution: AI assistants cannot directly execute or alter data without predefined, safe mechanisms (e.g., trigger GTT orders only if permitted).
Real-World Example: Zerodha & Kite MCP
Zerodha has implemented MCP with Claude and other AI tools. Their MCP server lets the AI assistant:
Read your live portfolio
Analyze investment allocations
Track profit/loss metrics
All without the assistant being able to place trades or access login credentials.
Hosting Your Own MCP Server
For developers and enterprises, running an MCP server typically involves:
Node.js or Python-based REST server
Secured endpoints exposing internal data
Proper authentication tokens
Context builders that shape responses for the AI assistant
Hosting (local or cloud)
Zerodha and others have made open-source templates available on GitHub to make this easier.
Future of MCP
The Model Context Protocol is a foundational step toward agentic AI—where models can proactively assist users with personal and business workflows in real time. As AI evolves from passive tools to active copilots, protocols like MCP will ensure they operate securely, intelligently, and with transparency.
In summary, MCP and MCP servers enable language models to move beyond static Q&A and truly integrate with the apps and services we use. This creates more personalized, real-time, and productive AI interactions—all while maintaining control, security, and modularity.
Let me know if you’d like a diagram or code sample for an MCP server setup.
