MCP vs OpenAI Function Calling: Which Should You Use?

If you're building AI applications that need to interact with external tools and services, you've likely encountered two major approaches: OpenAI Function Calling and Model Context Protocol (MCP). This guide breaks down the key differences and helps you choose the right one.

TL;DR — Quick Comparison

What is OpenAI Function Calling?

Function Calling (also called "Tools" in newer API versions) is OpenAI's approach to letting models use external functions. You define functions in your API call, the model decides when to call them, and you execute them in your application.

# OpenAI Function Calling Example
import openai

response = openai.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "What's the weather in NYC?"}],
    tools=[{
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather for a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {"type": "string"}
                },
                "required": ["location"]
            }
        }
    }]
)

# Model returns: tool_calls with function name + arguments
# You execute the function yourself
# Then send results back to continue conversation

Key point: Function Calling is a schema format, not an execution layer. The model outputs structured JSON saying "call this function with these args" — but you have to actually run the function and send results back.

What is MCP (Model Context Protocol)?

MCP is an open protocol created by Anthropic that standardizes how AI applications connect to external tools and data sources. Unlike Function Calling, MCP defines both the schema and the transport layer.

# MCP Server Example (using FastMCP)
from fastmcp import FastMCP

mcp = FastMCP("Weather Service")

@mcp.tool()
def get_weather(location: str) -> str:
    """Get current weather for a location."""
    # Your implementation here
    return f"Weather in {location}: 72°F, sunny"

# Server runs independently
# Any MCP-compatible client can connect and use it

Key point: MCP servers are standalone services that expose tools. Clients (like Claude Desktop, OpenClaw, or custom apps) connect to servers and use their tools. The execution happens on the server.

Key Differences

1. Architecture

Function Calling: Your application defines functions, sends them to OpenAI, receives structured output, executes functions locally, sends results back. It's a request-response pattern tightly coupled to your app.

MCP: Servers run independently and expose tools via a standard protocol. Clients discover and use tools dynamically. It's a service-oriented architecture — tools are decoupled from the AI application.

2. Provider Lock-in

Function Calling: The format is OpenAI-specific. If you switch to Claude or Gemini, you need to adapt your function definitions (though the concepts are similar).

MCP: Provider-agnostic by design. An MCP server works with any MCP-compatible client, regardless of which LLM it uses. Build once, use everywhere.

3. Reusability

Function Calling: Functions live in your application code. To reuse them elsewhere, you copy code or build internal libraries.

MCP: Servers are designed for reuse. There's a growing ecosystem of pre-built servers (filesystem, GitHub, databases, APIs). Install a server, connect it, done.

4. Execution Model

Function Calling: You control execution entirely. The model outputs "call get_weather('NYC')" and you decide how/when/whether to execute it.

MCP: The client invokes tools on the server. You still have control (clients can prompt for approval), but execution is delegated to the server.

When to Use Each

Real-World Example: GitHub Integration

Let's compare how you'd build a GitHub integration with each approach:

Function Calling Approach

# In your application
tools = [{
    "type": "function",
    "function": {
        "name": "create_github_issue",
        "description": "Create an issue on GitHub",
        "parameters": {
            "type": "object",
            "properties": {
                "repo": {"type": "string"},
                "title": {"type": "string"},
                "body": {"type": "string"}
            }
        }
    }
}]

# You implement the function
def create_github_issue(repo, title, body):
    return requests.post(
        f"https://api.github.com/repos/{repo}/issues",
        headers={"Authorization": f"token {GITHUB_TOKEN}"},
        json={"title": title, "body": body}
    )

# Handle tool calls in your main loop
if response.tool_calls:
    for call in response.tool_calls:
        if call.function.name == "create_github_issue":
            args = json.loads(call.function.arguments)
            result = create_github_issue(**args)
            # Send result back to model...

MCP Approach

# Install the official GitHub MCP server
# pip install mcp-server-github

# Configure your client to connect to it
# (e.g., in Claude Desktop's config)
{
  "mcpServers": {
    "github": {
      "command": "mcp-server-github",
      "env": {
        "GITHUB_TOKEN": "your-token"
      }
    }
  }
}

# Done. The client now has GitHub tools available.
# No custom code needed — the server handles everything.

The MCP approach requires less custom code because you're using a pre-built server. But if you need custom behavior, Function Calling gives you more control.

Can You Use Both?

Yes! They're not mutually exclusive. You might:

• • Use MCP servers for standard integrations (filesystem, databases)
• • Use Function Calling for app-specific tools that don't need reuse
• • Build MCP servers that wrap existing Function Calling implementations

Many agent frameworks support both. The choice often comes down to: "Is this tool specific to my app, or should it be reusable?"

The Future

MCP is newer (launched late 2024) but growing fast. Anthropic is pushing it as a standard, and there's momentum in the community. Function Calling isn't going away — it's deeply embedded in OpenAI's ecosystem.

My prediction: MCP becomes the standard for reusable tool servers, while Function Calling remains popular for app-specific integrations. Learn both.

Next Steps