MCP vs REST vs GraphQL: how llm-first apis are different

As AI integration grows, new standards are being developed for how models interact with data and services. While REST and GraphQL are familiar to most developers, MCP (Model Context Protocol) introduces a new approach designed for LLM-driven applications. This article explains the technical differences between MCP, REST, and GraphQL, helping junior developers understand how they compare and when each is used.

REST architecture overview

REST (Representational State Transfer) defines APIs through multiple endpoints, each representing a resource. Clients interact with resources using standard HTTP methods like GET, POST, PUT, and DELETE. REST is simple and stateless: each request contains all the information needed, and the server does not remember previous interactions.

• Pros: Simple, predictable, easy to cache.
• Cons: Over-fetching, under-fetching, limited real-time support.

Example:

GET /users/123

retrieves a user, while

POST /orders

creates an order.

Real-time updates require separate solutions (like WebSockets).

GraphQL query approach

GraphQL offers a single endpoint where clients specify exactly what data they need. It allows fetching nested or related data in one request, solving the over-fetching and under-fetching problems of REST.

• Pros: Fetch only what you need, strong schema, fewer round-trips.
• Cons: More complex server setup, requires learning GraphQL syntax.

Example query:

{
  user(id: "123") {
    name
    orders {
      id
      total
    }
  }
}

GraphQL supports real-time updates through subscriptions over WebSocket connections.

MCP and multi-protocol integration

MCP (Model Context Protocol) is different. It establishes a persistent session between an AI client (like a coding agent) and a server offering tools and data. Instead of calling fixed endpoints, the AI dynamically discovers available actions and invokes them as needed.

• Pros: Real-time, two-way communication, dynamic tool discovery, very flexible.
• Cons: More complex to set up, requires managing state.

Typical architecture:

• MCP Client: AI model (e.g., Claude desktop).
• MCP Server: Exposes tools and resources.
• Protocol Layer: Standardized communication between the two.

Example flow:

• The AI connects to a Postgres MCP server.
• It discovers a query_database tool.
• It uses that tool to fetch information without needing a hardcoded endpoint.

MCP also supports server-initiated events, making it well-suited for workflows where updates must be pushed immediately to the AI.

Key technical differences

Aspect REST GraphQL MCP Communication model Stateless, request–response Stateless, flexible query Stateful, persistent session Structure Multiple endpoints Single endpoint, dynamic fields Server exposes tools dynamically Real-time capabilities No (needs add-ons) Yes (subscriptions) Yes (built-in two-way messaging) Typical usage CRUD APIs Complex UI apps needing efficient data loading AI systems needing dynamic external access

Conclusion

REST, GraphQL, and MCP offer very different ways to connect systems:

• REST is the go-to for simple, predictable APIs.
• GraphQL shines when you need to customize exactly what data a client receives.
• MCP is new and fits use cases where AI agents need flexible, real-time access to tools and data.

If you’re building a traditional web service, REST is often enough. If you need efficient front-end data fetching, GraphQL may be better. But if you are building anything AI-driven — especially where dynamic tool use matters — MCP will likely become essential.

Understanding these protocols early in your career will give you an edge in choosing the right technology for your future projects.

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