MCP Tool Calling vs Function Calling: What Developers Need to Know

Two Approaches to the Same Problem

AI models cannot interact with the world directly. They need a mechanism to call external APIs, query databases, and perform actions. Two major approaches have emerged:

1. Proprietary function calling — Each model provider defines its own way (OpenAI functions, Anthropic tool use, Gemini function calling)
2. MCP tool calling — An open protocol that standardizes how any AI client calls any external service

How Function Calling Works

OpenAI's function calling works like this:

1. You define a function schema in your API request
2. The model decides if it should call a function
3. If yes, it returns the function name and arguments
4. Your code executes the function and sends the result back
5. The model incorporates the result into its response

{
  "model": "gpt-4",
  "tools": [{
    "type": "function",
    "function": {
      "name": "get_weather",
      "description": "Get weather for a city",
      "parameters": {
        "type": "object",
        "properties": {
          "city": { "type": "string" }
        }
      }
    }
  }]
}

Every model provider has its own format. Lock-in is built in.

How MCP Tool Calling Works

MCP tool calling inverts the relationship:

1. You define tools once in an MCP server
2. Any MCP client (Claude, Cursor, VS Code) discovers them automatically
3. The client calls tools through a standard JSON-RPC interface
4. Your API handles the request and returns results
5. The AI model receives the result — no custom code needed

The key difference: MCP is model-agnostic and client-agnostic. Define once, use everywhere.

Comparison Table

Feature	Function Calling	MCP Tool Calling
Protocol	Proprietary per provider	Open standard (JSON-RPC)
Client lock-in	Yes	No
Server management	You build it	Hosted or self-hosted
Discovery	Manual registration	Automatic tools/list
Transport	HTTP	HTTP, SSE, Stdio
Portability	None	Works across all MCP clients

When to Use Each

Use proprietary function calling if:

• You only use one model provider
• You need tight control over execution flow
• You are building a single-use integration

Use MCP tool calling if:

• You want your API to work with any AI client
• You are building a SaaS product
• You want to avoid vendor lock-in
• You want tools to be discovered automatically

Related Posts

• From REST to Reasoning: The Evolution of API Consumption with MCP
• MCP Protocol Explained: A Developer-Friendly Guide

The Bottom Line

Proprietary function calling is a feature of specific models. MCP is a standard for the entire AI ecosystem. If you are building for the long term, MCP is the better bet.

FuzeMCP handles the MCP protocol so you can build tools that work with any AI client — no per-provider integration needed.