REST APIs were designed for software developers writing static integrations. The Model Context Protocol (MCP) is built for artificial intelligence seeking dynamic, self-describing context.
1. The Cognitive Gap in Traditional APIs
When an AI agent connects to a REST API, it has to guess the meaning of JSON fields, handle authentication states blindly, and construct queries without knowing constraints. MCP solves this by introducing a stateful, descriptive layer.
Instead of reading documentation, the model queries the MCP server's tools, resources, and templates to dynamically understand what parameters it can send and what formats to expect.
2. JSON-RPC over Server-Sent Events (SSE)
MCP communicates using JSON-RPC 2.0. This allows bi-directional communication where the server can ask the AI client to perform verification steps or pull configuration schema, lowering the handshake overhead.
3. Implementing MCP in B2B Catalog Pipelines
By exposing your catalog via an MCP Node, you allow AI systems to securely query stock availability, custom discount rules, and payment logistics on behalf of the customer.