MCP vs. JSON APIs
Before fully appreciating the Model Context Protocol, it's helpful to understand the existing methods we use to make applications communicate. For years, the de facto standard for application-to-application (A2A) communication has been the RESTful API, with data exchanged primarily in JSON (JavaScript Object Notation) format.
In a typical JSON-based integration, one application (the client) sends an HTTP request to another application's API endpoint (the server). The server processes the request, often performing logic like querying a database or calling another service, and then returns a structured JSON response.
How it Works:
- Request: The client app sends an HTTP request (e.g., GET /api/users/123).
- Logic & Processing: The server application executes its internal business logic—authenticating the request, querying a database, applying transformations, etc.
- Response: The server sends back a JSON object containing the result (e.g.,
{ "id": 123, "name": "Alice", "email": "alice@example.com" }). - Client-Side Logic: The client application must then contain the logic to parse this JSON, handle potential errors, and integrate the data into its own workflow.
- Universality: JSON and HTTP are supported by virtually every programming language and platform.
- Explicit Control: The client has precise control over the request, and the server has precise control over the response and its underlying logic.
- Maturity: A vast ecosystem of tools, frameworks, and best practices exists for building and consuming REST APIs.
When we try to use this model to connect AI assistants to tools and data sources, several friction points emerge:
- Integration Overhead: For every new tool or data source, a developer must write custom integration code to connect the pieces. While the AI can generate JSON responses effectively, it lacks the ability to discover and invoke tools independently—the developer must manually wire everything together, creating a rigid pipeline where no dynamic tool discovery exists.