REST vs MCP for AI Agent Tools — Which Should You Use?

The Two Ways to Give Agents Tools

When you want to give an AI agent access to an external service — like Purple Flea's crypto APIs — you have two fundamental approaches:

REST API with function calling: Define tool schemas in OpenAI format, handle HTTP calls in your application code, and pass results back to the LLM as tool call results.
MCP Server: Connect a pre-built MCP server to your LLM client. The server exposes tools, resources, and prompts through the standardized Model Context Protocol.

Both approaches work. But they have very different tradeoffs, and the right choice depends on your use case, agent framework, and deployment model.

What is MCP?

The Model Context Protocol (MCP) is an open standard developed by Anthropic that defines how AI models connect to external data sources and tools. Think of it as a universal adapter: any LLM client that speaks MCP can connect to any MCP server — without writing custom integration code for each pair.

An MCP server exposes three types of capabilities:

Tools: Functions the AI can invoke (like place_trade or get_balance)
Resources: Data the AI can read (like wallet balances or market data)
Prompts: Pre-built prompt templates for common workflows

What is REST + Function Calling?

Traditional REST API integration involves defining tool schemas in OpenAI's JSON format (or equivalent), writing handler code that parses the LLM's tool call requests, makes HTTP requests to the target API, and returns results. This is the "classic" way agents have been integrated with external APIs since the introduction of function calling in 2023.

Purple Flea's LangChain and CrewAI packages use this approach: they define BaseTool classes that wrap REST API calls, with all the schema definition, HTTP handling, and error management done for you.

Head-to-Head Comparison

Dimension	REST + Function Calling	MCP Server
Framework Support	Universal (any LLM that supports tools)	Claude Desktop, Claude Code, growing list
Setup Complexity	Define schemas manually	Add server URL to config JSON
Streaming	Depends on implementation	Built-in (StreamableHTTP)
State Management	Managed by your code	Managed by MCP server
Error Handling	Your responsibility	Standardized MCP error types
Tool Discovery	Manual schema definition	Automatic via server manifest
Resource Subscriptions	Not supported	Built-in (watch market data)
Production Maturity	Very mature	Rapidly maturing (2024-2026)
Customization	Full control	Limited to server's exposed interface
Multi-LLM Support	Works with GPT-4, Claude, Gemini...	Primarily Claude (Anthropic's standard)

When to Use REST + Function Calling

✅ Use REST when...

Building with LangChain, CrewAI, AutoGen
Using non-Claude LLMs (GPT-4, Gemini, Llama)
Need custom tool schemas or behavior
Running programmatic agents (not interactive)
Want full control over error handling
Building production pipelines
Multi-model support is needed

❌ REST is harder when...

You want zero-boilerplate setup
Using Claude Desktop directly
You need real-time resource subscriptions
Building interactive Claude experiences
You prefer no-code/low-code tool connection

When to Use MCP

✅ Use MCP when...

Using Claude Desktop or Claude Code
Want instant setup (edit one JSON config)
Need real-time streaming tool responses
Building interactive agents for personal use
Want pre-built prompts and workflows
Using Smithery or other MCP registries

❌ MCP is harder when...

Using non-Claude LLMs programmatically
Need complex custom business logic
Deploying to serverless/cloud environments
Building multi-LLM agent systems
Need audit logging of all tool calls

Purple Flea Supports Both

Purple Flea gives you a choice — the same six products (trading, wallets, casino, domains, faucet, escrow) are accessible via both REST API and MCP servers:

REST API: LangChain Example

from purpleflea_langchain import PurpleFleatToolkit

toolkit = PurpleFleatToolkit(api_key="YOUR_KEY")
tools = toolkit.get_tools()  # Returns list of BaseTools

# Works with any LLM: GPT-4, Claude, Gemini, Llama
from langchain_openai import ChatOpenAI
from langchain.agents import create_openai_functions_agent

llm = ChatOpenAI(model="gpt-4o")
agent = create_openai_functions_agent(llm, tools, prompt)
    

MCP Server: Claude Desktop Config

// ~/.claude/claude_desktop_config.json
{
  "mcpServers": {
    "purpleflea-trading": {
      "url": "https://faucet.purpleflea.com/mcp",
      "transport": "http",
      "headers": {
        "X-API-Key": "YOUR_PURPLEFLEA_KEY"
      }
    },
    "purpleflea-escrow": {
      "url": "https://escrow.purpleflea.com/mcp",
      "transport": "http",
      "headers": {
        "X-API-Key": "YOUR_PURPLEFLEA_KEY"
      }
    }
  }
}

// That's it! Claude Desktop now has all Purple Flea tools.
// Say: "Check my BTC balance" and Claude handles the rest.
    

The Hybrid Approach: Best of Both

For complex production systems, the best architecture often combines both:

Use MCP for human-interactive sessions in Claude Desktop (quickly check balances, make small trades)
Use REST + LangChain for production agent pipelines that run autonomously on a schedule

Purple Flea supports this hybrid model because the underlying API is identical. The MCP server and the LangChain toolkit both call the same REST endpoints — they're just different clients for the same backend.

The Verdict

For most programmatic AI agents (LangChain, CrewAI, AutoGen, custom agents), REST with function calling is still the right choice. It's more flexible, works with every LLM, and gives you full control.

For Claude Desktop and interactive Claude sessions, MCP is dramatically easier — one JSON config line and you're done. No Python, no HTTP handling, no schema definitions.

Since Purple Flea offers both, you don't have to choose forever. Start with whichever fits your current setup, and switch or combine later.

Try Both — Free API Key

Get a Purple Flea API key and explore both the REST API (with LangChain/CrewAI) and MCP servers (with Claude Desktop).

Get Free API Key →