Comparison of Core Differences Between MCP and REST API

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

Architectural Differences

Feature	MCP	REST API
State Management	Stateful – maintains context across interactions	Stateless – each request is independent
Connection Type	Persistent, bidirectional connection	Unidirectional request/response
Communication Style	Continuous session based on JSON-RPC	Discrete requests based on HTTP
Context Handling	Context is intrinsic to the protocol	Context must be manually managed
Tool Discovery	Runtime discovery of available tools	Design-time integration; requires 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 layers of the technology stack:

1. REST: A low-level web communication pattern for exposing resource operations
2. MCP: A high-level AI protocol for orchestrating tool usage and maintaining context

MCP often uses REST APIs internally but abstracts them away for AI systems. Think of MCP as middleware that transforms discrete web services into a unified environment that AI agents can operate within.

Context Preservation: Key to AI Workflows

MCP’s stateful design addresses a critical limitation of REST in AI applications:

• REST approach: Each call is isolated, requiring manual context passing between steps
• MCP approach: Conversation context persists across multiple tool invocations

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

Dynamic Tool Discovery

MCP enables AI to discover and use tools at runtime:

// AI discovers available tools
{
  "tools": [
    {
      "name": "readFile",
      "description": "Read content from a file",
      "parameters": {
        "path": { "type": "string", "description": "File path" }
      }
    },
    {
      "name": "createTicket",
      "description": "Create a ticket in an 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.

Practical 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 across calls
• Any service API change breaks the workflow

MCP-based approach:

• Unified protocol for all tools
• Maintains context throughout the entire workflow
• New tools can be swapped in seamlessly without code changes

Why VJSP Uses MCP

VJSP leverages MCP to provide:

1. Extensibility: Add unlimited custom tools without waiting for official integrations
2. Context Awareness: Tools can access conversation history and project context
3. Simplified Integration: One standard protocol instead of multiple API paradigms
4. Runtime Flexibility: Dynamically discover and use new capabilities

MCP acts as a universal connector between VJSP and external services—REST APIs typically power those services behind the scenes.

Conclusion: Complementary, Not Competitive Technologies

MCP does not replace REST APIs—it builds upon them. REST excels at delivering discrete services, while MCP excels at orchestrating those services for AI agents.

The key 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 operate effectively in complex environments.