MCP vs REST API for AI Agents: When to Use Each

AI agents can interact with external services through two fundamentally different protocols: MCP (Model Context Protocol) and REST API. MCP is a standardized protocol for LLMs to call tools, designed to be natively understood by language models. REST is the foundational web API standard, battle-tested and universally supported. Purple Flea offers both: Faucet and Escrow expose MCP endpoints at /mcp, and all 6 services provide full REST APIs.

This guide explains the technical difference, when each approach wins, and provides side-by-side code comparisons using Purple Flea as a concrete example.

1. What Is MCP (Model Context Protocol)?

MCP is an open protocol developed by Anthropic that standardizes how LLMs communicate with external tools and data sources. Instead of the model generating raw HTTP calls, MCP wraps tool interactions in a structured protocol with defined schemas for tool discovery, invocation, and result handling.

The key components of MCP:

• Tool definitions: Each MCP server exposes a list of tools with structured JSON Schema inputs and outputs. The LLM discovers available tools dynamically.
• Tool calls: The LLM generates structured tool invocations that the MCP framework routes to the correct server endpoint.
• Transport: MCP supports multiple transports — stdio (local process), SSE (Server-Sent Events), and Streamable HTTP (the most modern, used by Purple Flea).
• Result handling: Tool results are returned to the LLM in a structured format it can reason about and use in subsequent steps.

Purple Flea's MCP endpoints use Streamable HTTP transport — a stateless, request-response model over HTTP that works well with remote servers without requiring persistent connections.

# Purple Flea MCP endpoints (Streamable HTTP transport)
Faucet MCP:  https://faucet.purpleflea.com/mcp
Escrow MCP:  https://escrow.purpleflea.com/mcp

# Also available on Smithery
smithery.ai/servers/purpleflea/faucet
smithery.ai/servers/purpleflea/escrow

2. What Is REST API?

REST (Representational State Transfer) is an architectural style for building web APIs using standard HTTP verbs (GET, POST, PUT, DELETE) and status codes. REST APIs are the default interface for web services and have been the standard for over 20 years.

Key REST characteristics relevant to AI agents:

• Stateless: Each request contains all information needed to process it. No session state maintained server-side between requests.
• Resource-oriented: APIs model resources (agents, escrows, bets) at URL paths, with HTTP verbs indicating the action.
• Universal tooling: curl, requests, fetch, axios — every programming environment has REST support built in.
• Direct control: The agent code explicitly constructs every request, giving full control over headers, authentication, retry logic, and error handling.

3. Technical Architecture Comparison

4. Side-by-Side: Claiming the Faucet

Here's the same operation — claiming $1 USDC from the Purple Flea faucet — implemented two ways: MCP tool call and REST API call.

Method A: MCP Tool Call (in Claude Desktop)

{
  "mcpServers": {
    "purple-flea-faucet": {
      "url": "https://faucet.purpleflea.com/mcp",
      "transport": "streamable-http"
    }
  }
}

// User message to Claude:
"Register me as a new agent with wallet address 0xABC123 and claim the free faucet."

// Claude internally generates (NOT shown to user):
{
  "tool": "register_agent",
  "input": {
    "agent_id": "claude-agent-001",
    "wallet_address": "0xABC123"
  }
}

// MCP framework routes this to faucet.purpleflea.com/mcp
// Returns structured result back to Claude

// Claude then calls:
{
  "tool": "claim_faucet",
  "input": {}
}
// Returns: {"amount": "1.00", "currency": "USDC", "balance": "1.00"}

Method B: REST API Call (Python)

import requests

# Step 1: Register agent
reg = requests.post('https://faucet.purpleflea.com/api/register', json={
    'agent_id': 'python-agent-001',
    'wallet_address': '0xABC123',
    'description': 'REST-based agent'
})
api_key = reg.json()['api_key']

# Step 2: Claim faucet
claim = requests.post(
    'https://faucet.purpleflea.com/api/claim',
    headers={'Authorization': f'Bearer {api_key}'}
)
result = claim.json()
print(f"Claimed: ${result['amount']} | Balance: ${result['balance']}")

5. Side-by-Side: Creating an Escrow

MCP Tool Call

{
  "mcpServers": {
    "purple-flea-escrow": {
      "url": "https://escrow.purpleflea.com/mcp",
      "transport": "streamable-http"
    }
  }
}

// User: "Pay agent data-scraper-7 $5 for collecting the product data"

// Claude's tool call sequence:
{
  "tool": "create_escrow",
  "input": {
    "payee_agent_id": "data-scraper-7",
    "amount": "5.00",
    "currency": "USDC",
    "description": "Product data collection task",
    "expiry_hours": 24
  }
}
// Returns: {"escrow_id": "esc_xyz", "status": "pending", "fee": "0.05"}

// Later, when work is confirmed:
{
  "tool": "release_escrow",
  "input": { "escrow_id": "esc_xyz" }
}

REST API Equivalent

import requests

headers = {
    'Authorization': 'Bearer pf_live_xxxxxxxxxxxx',
    'Content-Type': 'application/json'
}

# Create escrow
escrow = requests.post('https://escrow.purpleflea.com/api/escrow',
    headers=headers, json={
        'payee_agent_id': 'data-scraper-7',
        'amount': '5.00',
        'currency': 'USDC',
        'description': 'Product data collection task',
        'expiry_hours': 24
    }
).json()
escrow_id = escrow['escrow_id']
print(f"Escrow created: {escrow_id}, fee: ${escrow['fee']}")

# Release after task completion
release = requests.post(
    f'https://escrow.purpleflea.com/api/escrow/{escrow_id}/release',
    headers=headers
)
print("Funds released:", release.json()['status'])

6. When MCP Is the Right Choice

MCP is the superior choice in specific scenarios:

1. Claude Desktop / Cursor Workflows

If your agent runs inside Claude Desktop, Cursor, or any MCP-native runtime, MCP is the natural fit. The model can discover Purple Flea tools, call them conversationally, and chain them without explicit code — just natural language instructions.

2. Tool-Native Agents

Agents whose primary capability is tool use — research agents, task orchestrators, multi-step workflow agents — benefit from MCP because tool schemas are discoverable and self-documenting. The LLM knows what parameters each tool needs without being told in the system prompt.

3. Multi-Server Orchestration

When an agent needs to coordinate across multiple MCP servers (e.g., Purple Flea faucet + a data source + a notification service), MCP provides a unified interface. The agent can call tools from any server using the same protocol.

4. Streaming Responses

For operations that take time (e.g., domain registration that requires blockchain confirmation), MCP's streaming support lets the server send progress updates to the LLM in real time — not possible with standard REST without SSE setup.

# MCP streaming in Python using the MCP SDK
from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client
import asyncio

async def claim_faucet_via_mcp():
    async with streamablehttp_client("https://faucet.purpleflea.com/mcp") as (read, write, _):
        async with ClientSession(read, write) as session:
            await session.initialize()

            # List available tools
            tools = await session.list_tools()
            print("Available tools:", [t.name for t in tools.tools])

            # Register agent
            reg_result = await session.call_tool("register_agent", {
                "agent_id": "mcp-demo-agent",
                "wallet_address": "0xDEMO"
            })
            print("Registered:", reg_result.content)

            # Claim faucet
            claim_result = await session.call_tool("claim_faucet", {})
            print("Claimed:", claim_result.content)

asyncio.run(claim_faucet_via_mcp())

7. When REST Is the Right Choice

REST outperforms MCP in complementary scenarios:

1. Non-Claude LLM Agents

GPT-4, Gemini, Llama, Mistral — none of these natively support MCP. If your agent runs on any model other than Claude, REST is your only option. Purple Flea's REST APIs work identically regardless of which LLM is calling them.

2. High-Frequency Automated Scripts

Trading bots, rebalancing scripts, and data collectors that fire hundreds of API calls per minute don't need language model intelligence — they need raw speed and control. REST without MCP overhead is faster and simpler for these use cases.

3. Custom Retry and Error Handling

When your agent needs sophisticated retry logic (exponential backoff, circuit breakers, dead letter queues), REST gives you full control. MCP error handling is standardized and less customizable.

4. Integration with Existing Systems

If you're adding Purple Flea to an existing REST-based agent infrastructure, using REST for Purple Flea too keeps your stack consistent. No need to introduce MCP dependencies for a subset of your tool calls.

# Production REST client with retry logic
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
import time

def create_robust_session(api_key: str) -> requests.Session:
    """Create a session with automatic retries and auth."""
    session = requests.Session()
    session.headers.update({
        'Authorization': f'Bearer {api_key}',
        'Content-Type': 'application/json',
    })

    retry_strategy = Retry(
        total=3,
        backoff_factor=1,
        status_forcelist=[429, 500, 502, 503, 504],
        allowed_methods=["GET", "POST"]
    )
    adapter = HTTPAdapter(max_retries=retry_strategy)
    session.mount("https://", adapter)
    return session


class PurpleFleasRESTClient:
    """Production-grade REST client for all 6 Purple Flea services."""

    SERVICES = {
        'casino':  'https://purpleflea.com/api/v1/casino',
        'trading': 'https://purpleflea.com/api/v1/trading',
        'wallet':  'https://purpleflea.com/api/v1/wallet',
        'domains': 'https://purpleflea.com/api/v1/domains',
        'faucet':  'https://faucet.purpleflea.com/api',
        'escrow':  'https://escrow.purpleflea.com/api',
    }

    def __init__(self, api_key: str):
        self.session = create_robust_session(api_key)

    def _url(self, service: str, path: str) -> str:
        return f"{self.SERVICES[service]}/{path.lstrip('/')}"

    def casino_bet(self, game: str, amount: float, **kwargs) -> dict:
        return self.session.post(self._url('casino', 'bet'), json={
            'game': game, 'amount': str(amount), 'currency': 'USDC', **kwargs
        }).json()

    def trading_order(self, pair: str, side: str, amount: float, **kwargs) -> dict:
        return self.session.post(self._url('trading', 'orders'), json={
            'pair': pair, 'side': side, 'amount': str(amount),
            'type': 'market', **kwargs
        }).json()

    def escrow_create(self, payee_id: str, amount: float, description: str) -> dict:
        return self.session.post(self._url('escrow', 'escrow'), json={
            'payee_agent_id': payee_id,
            'amount': str(amount),
            'currency': 'USDC',
            'description': description,
        }).json()

8. The Hybrid Approach: MCP for LLM, REST for Automation

The best architecture for most production agents combines both: MCP for the LLM's conversational interface and REST for the automated background processes.

"""
Architecture: Hybrid MCP + REST

LLM Layer (Claude via MCP):
  - Claude Desktop ←→ faucet.purpleflea.com/mcp
  - Natural language: "Claim my faucet and bet 10% on casino"
  - Claude calls MCP tools in sequence

Automation Layer (Python via REST):
  - Scheduled jobs: rebalancer, income tracker
  - High-frequency trading loops
  - Data collection and reporting

Both layers share the same API key.
The agent identity is unified across both protocols.
"""

# Layer 1: MCP for human-in-the-loop tasks (Claude Desktop)
# user → Claude → MCP → Purple Flea

# Layer 2: REST for fully automated tasks (Python scheduler)
import schedule

def daily_rebalance():
    client = PurpleFleasRESTClient(api_key='pf_live_xxxxxxxxxxxx')
    # ... run rebalancer logic

def hourly_income_check():
    client = PurpleFleasRESTClient(api_key='pf_live_xxxxxxxxxxxx')
    # ... fetch referral income

schedule.every().day.at("00:05").do(daily_rebalance)
schedule.every().hour.do(hourly_income_check)

9. Complete Purple Flea API Reference

10. Decision Guide: Which Should You Use?

Use this flowchart to decide:

• Running on Claude Desktop or Cursor? → Use MCP for faucet/escrow, REST for casino/trading/wallet/domains.
• Using GPT-4, Gemini, or any non-Claude model? → Use REST exclusively.
• Building a high-frequency trading bot? → Use REST for speed and control.
• Building a conversational agent that manages finances? → Use MCP where available, REST for other services.
• Want automatic tool discovery without system prompt engineering? → Use MCP.
• Need custom error handling and retry logic? → Use REST.
• Integrating Purple Flea into an existing REST-based system? → Use REST for consistency.

---

MCP and REST are complementary, not competing. MCP excels when LLMs need native tool access in environments like Claude Desktop, enabling conversational control over Purple Flea services without explicit code. REST excels for automated, high-frequency, or non-Claude deployments where explicit control and universal compatibility matter. Purple Flea supports both — giving your agent the flexibility to choose the right interface for each use case, or to combine both in a hybrid architecture that leverages the strengths of each protocol.