|
| 1 | +--- |
| 2 | +title: MCP vs API |
| 3 | +sidebar_label: MCP vs API |
| 4 | +--- |
| 5 | + |
| 6 | +# MCP vs REST APIs: A Fundamental Distinction |
| 7 | + |
| 8 | +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. |
| 9 | + |
| 10 | +## Architectural Differences |
| 11 | + |
| 12 | +| Feature | MCP | REST APIs | |
| 13 | +|---------|-----|-----------| |
| 14 | +| State Management | **Stateful** - maintains context across interactions | **Stateless** - each request is independent | |
| 15 | +| Connection Type | Persistent, bidirectional connections | One-way request/response | |
| 16 | +| Communication Style | JSON-RPC based with ongoing sessions | HTTP-based with discrete requests | |
| 17 | +| Context Handling | Context is intrinsic to the protocol | Context must be manually managed | |
| 18 | +| Tool Discovery | Runtime discovery of available tools | Design-time integration requiring prior knowledge | |
| 19 | +| Integration Approach | Runtime integration with dynamic capabilities | Design-time integration requiring code changes | |
| 20 | + |
| 21 | +## Different Layers, Different Purposes |
| 22 | + |
| 23 | +REST APIs and MCP serve different tiers in the technology stack: |
| 24 | + |
| 25 | +1. **REST**: Low-level web communication pattern that exposes operations on resources |
| 26 | +2. **MCP**: High-level AI protocol that orchestrates tool usage and maintains context |
| 27 | + |
| 28 | +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. |
| 29 | + |
| 30 | +## Context Preservation: Critical for AI Workflows |
| 31 | + |
| 32 | +MCP's stateful design solves a key limitation of REST in AI applications: |
| 33 | + |
| 34 | +- **REST Approach**: Each call is isolated, requiring manual context passing between steps |
| 35 | +- **MCP Approach**: One conversation context persists across multiple tool uses |
| 36 | + |
| 37 | +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. |
| 38 | + |
| 39 | +## Dynamic Tool Discovery |
| 40 | + |
| 41 | +MCP enables an AI to discover and use tools at runtime: |
| 42 | + |
| 43 | +```json |
| 44 | +// AI discovers available tools |
| 45 | +{ |
| 46 | + "tools": [ |
| 47 | + { |
| 48 | + "name": "readFile", |
| 49 | + "description": "Reads content from a file", |
| 50 | + "parameters": { |
| 51 | + "path": { "type": "string", "description": "File path" } |
| 52 | + } |
| 53 | + }, |
| 54 | + { |
| 55 | + "name": "createTicket", |
| 56 | + "description": "Creates a ticket in issue tracker", |
| 57 | + "parameters": { |
| 58 | + "title": { "type": "string" }, |
| 59 | + "description": { "type": "string" } |
| 60 | + } |
| 61 | + } |
| 62 | + ] |
| 63 | +} |
| 64 | +``` |
| 65 | + |
| 66 | +This "plug-and-play" capability allows new tools to be added without redeploying or modifying the AI itself. |
| 67 | + |
| 68 | +## Real-World Example: Multi-Tool Workflow |
| 69 | + |
| 70 | +Consider a task requiring multiple services: "Check recent commits, create a JIRA ticket for the bug fix, and post to Slack." |
| 71 | + |
| 72 | +**REST-based approach**: |
| 73 | +- Requires separate integrations for Git, JIRA, and Slack APIs |
| 74 | +- Needs custom code to manage context between calls |
| 75 | +- Breaks if any service changes its API |
| 76 | + |
| 77 | +**MCP-based approach**: |
| 78 | +- One unified protocol for all tools |
| 79 | +- Maintains context across the entire workflow |
| 80 | +- New tools can be swapped in without code changes |
| 81 | + |
| 82 | +## Why Roo Code Uses MCP |
| 83 | + |
| 84 | +Roo Code leverages MCP to provide: |
| 85 | + |
| 86 | +1. **Extensibility**: Add unlimited custom tools without waiting for official integration |
| 87 | +2. **Contextual awareness**: Tools can access conversation history and project context |
| 88 | +3. **Simplified integration**: One standard protocol rather than numerous API patterns |
| 89 | +4. **Runtime flexibility**: Discover and use new capabilities on-the-fly |
| 90 | + |
| 91 | +MCP creates a universal connector between Roo Code and external services, with REST APIs often powering those services behind the scenes. |
| 92 | + |
| 93 | +## Conclusion: Complementary, Not Competing Technologies |
| 94 | + |
| 95 | +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. |
| 96 | + |
| 97 | +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. |
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