Model Context Protocol (MCP)
The Model Context Protocol (MCP) is an open standard that functions as a universal connector between AI assistants and external systems. It standardizes how AI models interact with data repositories and business tools, effectively replacing fragmented, custom integrations with a single, unified protocol.
The “USB-C” for Artificial Intelligence
Think of MCP as a USB-C port for AI applications.
- Before MCP: Connecting an AI to a database required writing a specific “cable” (custom API glue code) for every single connection.
- With MCP: Developers build a connector once, and it works seamlessly across multiple AI platforms (like Claude, IDEs, or Agentic workflows).
How It Works
Technologically, MCP operates on a Client-Host-Server architecture:
- The Host: The application running the AI (e.g., the Claude Desktop app or an IDE).
- The Server: The data source (e.g., a local file system, a Slack workspace, or a PostgreSQL database).
- The Protocol: They communicate using JSON-RPC 2.0, typically transported via stdio for local connections or Server-Sent Events (SSE) for remote connections.
From Static to “Just-in-Time” RAG
While MCP is a critical enabler for Retrieval-Augmented Generation (RAG), it represents a fundamental shift in how agents access knowledge.
| Feature | Traditional RAG | MCP (Dynamic RAG) |
|---|---|---|
| Data Source | Pre-indexed Vector Databases | Live “Resources” & “Tools” |
| Freshness | Snapshots (Can become stale) | Real-time (Source of Truth) |
| Mechanism | Semantic Search | Direct Query / Function Execution |
By allowing the model to query a live SQL database or read the current state of a git repository at the exact moment of inference, MCP enables “Just-in-Time” intelligence. This removes the reliance on stale data dumps and allows agents to act on the absolute latest state of the world.