MCP vs REST APIs: A Fundamental Distinction

Comparing REST APIs to the Model Context Protocol (MCP) is a category error. They operate at different layers of abstraction and serve fundamentally different purposes in AI systems.

Architectural Differences

Feature	MCP	REST APIs
State Management	Stateful - maintains context across interactions	Stateless - each request is independent
Connection Type	Persistent, bidirectional connections	One-way request/response
Communication Style	JSON-RPC based with ongoing sessions	HTTP-based with discrete requests
Context Handling	Context is intrinsic to the protocol	Context must be manually managed
Tool Discovery	Runtime discovery of available tools	Design-time integration requiring prior knowledge
Integration Approach	Runtime integration with dynamic capabilities	Design-time integration requiring code changes

Different Layers, Different Purposes

REST APIs and MCP serve different tiers in the technology stack:

1. REST: Low-level web communication pattern that exposes operations on resources
2. MCP: High-level AI protocol that orchestrates tool usage and maintains context

MCP often uses REST APIs internally, but abstracts them away for the AI. Think of MCP as middleware that turns discrete web services into a cohesive environment the AI can operate within.

Context Preservation: Critical for AI Workflows

MCP's stateful design solves a key limitation of REST in AI applications:

• REST Approach: Each call is isolated, requiring manual context passing between steps
• MCP Approach: One conversation context persists across multiple tool uses

For example, an AI debugging a codebase can open a file, run tests, and identify errors without losing context between steps. The MCP session maintains awareness of previous actions and results.

Dynamic Tool Discovery

MCP enables an AI to discover and use tools at runtime:

// AI discovers available tools
{
  "tools": [
    {
      "name": "readFile",
      "description": "Reads content from a file",
      "parameters": {
        "path": { "type": "string", "description": "File path" }
      }
    },
    {
      "name": "createTicket",
      "description": "Creates a ticket in issue tracker",
      "parameters": {
        "title": { "type": "string" },
        "description": { "type": "string" }
      }
    }
  ]
}

This "plug-and-play" capability allows new tools to be added without redeploying or modifying the AI itself.

Real-World Example: Multi-Tool Workflow

Consider a task requiring multiple services: "Check recent commits, create a JIRA ticket for the bug fix, and post to Slack."

REST-based approach:

• Requires separate integrations for Git, JIRA, and Slack APIs
• Needs custom code to manage context between calls
• Breaks if any service changes its API

MCP-based approach:

• One unified protocol for all tools
• Maintains context across the entire workflow
• New tools can be swapped in without code changes

Why Roo Code Uses MCP

Roo Code leverages MCP to provide:

1. Extensibility: Add unlimited custom tools without waiting for official integration
2. Contextual awareness: Tools can access conversation history and project context
3. Simplified integration: One standard protocol rather than numerous API patterns
4. Runtime flexibility: Discover and use new capabilities on-the-fly

MCP creates a universal connector between Roo Code and external services, with REST APIs often powering those services behind the scenes.

Conclusion: Complementary, Not Competing Technologies

MCP doesn't replace REST APIs - it builds upon them. REST excels at providing discrete services, while MCP excels at orchestrating those services for AI agents.

The critical distinction is that MCP is AI-native: it treats the model as a first-class user, providing the contextual, stateful interaction layer that AI agents need to function effectively in complex environments.