REST APIs vs MCP for AI Agents: What Changed and When to Use Which

If you are building AI-powered applications in 2026, you have probably heard about MCP (Model Context Protocol). Some articles call it "the death of REST APIs." Others say it is just a wrapper. Both are wrong.

REST APIs and MCP operate at different layers of the stack. They are complementary, not competing. But understanding when to use each one, and why MCP exists at all, is essential for developers building AI agents, chatbots, and copilots.

This guide breaks down what MCP actually is, how it compares to REST for AI agent integration, and when each approach makes sense. No hype, just the technical trade-offs.

The Problem MCP Solves

REST APIs are designed for server-to-server communication. A developer reads the documentation, writes HTTP client code, handles authentication, parses responses, and manages errors. This workflow has worked for 20+ years and still works today.

But AI agents are not developers. They do not read documentation. They need to discover what tools are available, understand what parameters each tool expects, call the right tool for the right task, and handle responses, all at runtime without human intervention.

Before MCP, connecting an AI agent to an API required significant manual work:

1. Read the API documentation
2. Write a tool definition for each endpoint the agent should use
3. Parse the API response and format it for the LLM
4. Handle authentication, errors, and rate limits
5. Maintain this code when the API changes
6. Repeat for every API the agent needs

For an astrology API with 85+ endpoints, that means writing 85+ tool definitions manually. For a multi-API agent that uses astrology, weather, and calendar services, you are maintaining hundreds of tool definitions across multiple providers.

MCP eliminates this entire layer of manual work.

What MCP Actually Is

Model Context Protocol is an open standard developed by Anthropic (released late 2024) that defines how AI applications communicate with external tools and data sources. Think of it as a universal connector between AI agents and APIs.

Here is how MCP works at a technical level:

1. Tool Discovery

When an AI agent connects to an MCP server, it sends a tools/list request. The server responds with every available tool, including its name, description, and input schema:

{
  "tools": [
    {
      "name": "generate_natal_chart",
      "description": "Generate a natal birth chart with planetary positions, house cusps, and aspects",
      "inputSchema": {
        "type": "object",
        "properties": {
          "date": { "type": "string", "description": "Birth date (YYYY-MM-DD)" },
          "time": { "type": "string", "description": "Birth time (HH:MM)" },
          "latitude": { "type": "number", "description": "Birth location latitude" },
          "longitude": { "type": "number", "description": "Birth location longitude" },
          "timezone": { "type": "number", "description": "UTC timezone offset" }
        },
        "required": ["date", "time", "latitude", "longitude", "timezone"]
      }
    }
  ]
}

The AI agent now knows what this tool does, what parameters it needs, and what types those parameters should be. No documentation reading required.

2. Tool Calling

When the AI decides it needs to generate a birth chart (based on the user's natural language request), it sends a tools/call request:

{
  "method": "tools/call",
  "params": {
    "name": "generate_natal_chart",
    "arguments": {
      "date": "1990-06-15",
      "time": "14:30",
      "latitude": 40.7128,
      "longitude": -74.0060,
      "timezone": -4
    }
  }
}

The MCP server calls the underlying API, gets the response, and returns it to the agent in a structured format the LLM can interpret.

3. Context Preservation

Unlike REST (which is stateless by design), MCP maintains a persistent session. The AI agent can call multiple tools in sequence while preserving context from previous calls. This is critical for multi-step workflows like: "Generate my birth chart, then check my current transits, then tell me how today's planetary positions affect my natal placements."

REST vs MCP: Side-by-Side Comparison

Aspect	REST API	MCP
Design purpose	Server-to-server communication	AI agent-to-tool communication
State management	Stateless (each request independent)	Stateful (session context persists)
Tool discovery	Manual (read docs, write code)	Automatic (runtime discovery)
Connection type	One-way request/response	Persistent, bidirectional
Protocol	HTTP with JSON	JSON-RPC over HTTP/SSE
Integration effort	Write custom code per endpoint	Zero-config connection
Adding new tools	Requires code changes and redeployment	Agent discovers new tools automatically
Error handling	HTTP status codes, custom parsing	Standardized error format
Authentication	Varies (API key, OAuth, JWT)	Configured once at connection level
Best for	Direct API calls, microservices, webhooks	AI agents, chatbots, copilots

The Same Task, Two Approaches

Let us compare building an AI astrology chatbot using REST versus MCP.

REST Approach: Manual Tool Definitions

With REST, you need to define every tool the AI agent can use:

# Define tools manually for each endpoint
tools = [
    {
        "type": "function",
        "function": {
            "name": "generate_natal_chart",
            "description": "Generate a birth chart",
            "parameters": {
                "type": "object",
                "properties": {
                    "date": {"type": "string"},
                    "time": {"type": "string"},
                    "latitude": {"type": "number"},
                    "longitude": {"type": "number"},
                    "timezone": {"type": "number"}
                }
            }
        }
    },
    # ... repeat for every endpoint you want the agent to use
]

# Handle tool calls manually
def handle_tool_call(name, args):
    if name == "generate_natal_chart":
        response = requests.post(
            "https://api.example.com/v2/astrology/natal-chart",
            headers={"X-API-Key": API_KEY},
            json=args
        )
        return response.json()
    elif name == "draw_tarot_cards":
        response = requests.post(
            "https://api.example.com/v2/tarot/draw",
            headers={"X-API-Key": API_KEY},
            json=args
        )
        return response.json()
    # ... repeat for every tool

For 85+ endpoints, this is hundreds of lines of boilerplate code. When the API adds new endpoints, you update the tool definitions and handler code manually.

MCP Approach: Zero Configuration

With MCP, the entire integration is:

from agents import Agent
from agents.mcp import MCPServerSse

agent = Agent(
    name="astrology_assistant",
    instructions="You are an expert astrologer...",
    mcp_servers=[MCPServerSse(
        url="https://roxyapi.com/mcp/astrology-api",
        headers={"X-API-Key": API_KEY}
    )]
)

That is the complete integration. The agent automatically discovers all available tools, knows their parameters, and can call them. When the API adds new endpoints, the agent discovers them on the next connection without any code changes.

The Difference in Practice

Metric	REST Approach	MCP Approach
Integration code	200-500 lines per API	5-10 lines per API
Time to integrate	Hours to days	Minutes
New endpoint support	Manual code update + deploy	Automatic discovery
Multi-API agent	Separate handler per provider	Add another MCP server URL
Maintenance burden	High (update per API change)	Near zero

When REST Is Still the Right Choice

MCP is not a universal replacement for REST. There are clear cases where REST remains the better approach:

Direct server-to-server calls. If your backend needs to call an API endpoint with specific parameters and process the response in code you control, REST is simpler and more direct. MCP adds a layer of abstraction you do not need.

Webhooks and event-driven architectures. MCP is request-response oriented. For push-based notifications (payment webhooks, real-time updates), REST endpoints are the standard.

Simple, deterministic workflows. If your application always calls the same three endpoints in the same order with the same logic, manually coding those three calls is faster than setting up an MCP connection.

Batch processing. Sending 10,000 requests in parallel with custom retry logic, rate limiting, and error handling is a REST workflow. MCP is designed for interactive, agent-driven flows.

Existing infrastructure. If your stack is built on REST with API gateways, load balancers, and monitoring, there is no reason to replace that with MCP. MCP sits on top of REST, not instead of it.

When MCP Is the Right Choice

MCP shines when the AI agent needs to make decisions about which tools to use:

AI chatbots and assistants. When users ask natural language questions and the AI needs to figure out which API calls to make. "What does my birth chart say about my career?" might require a natal chart call, a transit call, and a house analysis call, and the AI decides this at runtime.

Multi-tool orchestration. When an agent needs to combine data from multiple endpoints or multiple APIs in a single workflow. MCP makes adding new tool sources trivial.

Dynamic tool selection. When you cannot predict at design time which endpoints the agent will need. A spiritual wellness chatbot might need astrology, tarot, numerology, or dream interpretation depending on what the user asks.

Rapid prototyping. When you want to connect an AI agent to an API and test it in minutes rather than spending hours writing tool definitions.

Evolving APIs. When the API provider adds new endpoints regularly and you want the agent to use them without redeploying your code.

Real-World Example: Astrology AI Agent

Consider building an AI astrology assistant that handles this conversation:

User: "I was born June 15, 1990 at 2:30 PM in New York. What are my strengths?"

Agent thinks: I need to generate a natal chart for this birth data, then analyze the planetary positions and aspects for strengths.

Agent calls: generate_natal_chart with the birth data

Agent calls: get_planetary_aspects for aspect patterns

Agent responds: "Your Sun in Gemini gives you exceptional communication skills, and your Moon in Aries adds initiative and courage..."

With REST, you pre-define which tools the agent can use and write handlers for each one. With MCP, the agent discovers all 85+ endpoints and chooses the right ones for each conversation.

The real advantage shows when the user follows up:

User: "Can you also draw me a tarot card for today?"

With REST, this only works if you pre-defined tarot tools alongside the astrology tools. With MCP, if the tarot API is connected as another MCP server, the agent discovers and uses the tarot tools automatically.

RoxyAPI provides native MCP servers for all six of its intelligence domains (Western astrology, Vedic astrology, tarot, numerology, dreams, I-Ching). Connect one or all of them to your AI agent, and every endpoint becomes a callable tool with zero integration code.

The Ecosystem in 2026

MCP adoption has grown rapidly since its introduction. Here is where support stands:

AI platforms with MCP support:

• OpenAI Agents SDK
• Claude Desktop and Claude Code
• Google Gemini via ADK (Agent Development Kit)
• Cursor IDE
• Windsurf IDE
• VS Code (GitHub Copilot, Cline, and other extensions)
• Zed editor

API providers with native MCP:

• RoxyAPI (astrology, tarot, numerology, dreams, I-Ching)
• Stripe (payments)
• Cloudflare (infrastructure)
• A growing list of SaaS tools and data providers

MCP server frameworks:

• Official TypeScript SDK (@modelcontextprotocol/sdk)
• Python SDK
• Community implementations in Go, Rust, Java

The standard is still maturing. New capabilities like streaming, batch operations, and enhanced security are being developed. But the core protocol (tool discovery, tool calling, context management) is stable and production-ready.

Getting Started

If you are building an AI agent today, here is a practical decision framework:

1. Is an AI model choosing which tools to call? Use MCP.
2. Is your code calling APIs in a predetermined sequence? Use REST.
3. Do you need both? Many production systems use MCP for the agent layer and REST for backend services. They coexist naturally.

For developers building AI-powered astrology, tarot, or numerology applications, RoxyAPI's MCP documentation shows how to connect in under 2 minutes with OpenAI, Claude, or Gemini. The same API works via standard REST for server-to-server calls. You are not locked into one approach.

View pricing or explore the API documentation.

Frequently Asked Questions

Q: Does MCP replace REST APIs? A: No. MCP builds on top of REST. MCP servers often call REST APIs internally. The two protocols operate at different layers: REST handles HTTP communication, MCP handles AI agent-to-tool orchestration. Most production systems use both.

Q: Is MCP only for Anthropic/Claude? A: No. MCP is an open standard that works with OpenAI Agents, Google Gemini (via ADK), Cursor, Windsurf, VS Code, and any application that implements the protocol. Anthropic developed it, but it is not proprietary to their products.

Q: Does MCP add latency compared to direct REST calls? A: MCP adds minimal overhead (typically under 10ms) for the protocol layer. The actual API call takes the same time whether it goes through MCP or direct REST. For AI agent workflows where the LLM response takes 1-5 seconds, the MCP overhead is negligible.

Q: Can I use MCP without an AI agent? A: Technically yes, but there is little benefit. MCP is designed for AI-driven tool discovery and calling. If your code knows exactly which endpoints to call, direct REST is simpler.

Q: How does authentication work with MCP? A: Authentication is configured once at the MCP connection level. You provide your API key (or other credentials) when connecting to the MCP server. All subsequent tool calls through that connection use the same authentication. No per-request auth handling needed.

Q: What happens when an API adds new endpoints? A: With MCP, the AI agent discovers new tools automatically on the next connection. No code changes, no redeployment, no manual tool definition updates. With REST, you need to update your tool definitions and handler code manually.

Q: Which astrology APIs support MCP natively? A: As of February 2026, RoxyAPI is the only astrology and prediction API with native MCP support. Other providers require developers to build custom MCP servers or write manual tool definitions for their REST endpoints.

Q: Is MCP secure? A: MCP uses HTTPS/TLS for transport security. API keys are transmitted in headers over encrypted connections. The MCP specification includes security recommendations for input validation, access control, and rate limiting. Credentials are stored locally in your MCP client configuration and never exposed in code.