MCP or Function Calling: Everything You Need To Know

Are you tired of your AI assistant giving you outdated information or saying "I can't access that" when you need real-time data?

You've tried asking AI about your business data it doesn't know. You want it to check your emails or calendar it can't. You need it to look up information from your company systems which is impossible.

Here's the problem: Most AI tools are disconnected from everything else you use.

It's like having a really smart assistant who's locked in a separate room with no phone, no computer, and no way to access any of your files. They can answer general questions all day, but they can't help with the specific stuff that matters to you.

This is why you end up copying information back and forth, doing the work yourself, and wondering why AI isn't as helpful as everyone promised.

But there are ways to fix this. Two popular solutions are called "Model Context Protocol (MCP)" and "Function Calling." These are just fancy names for methods that let AI connect to your tools and data.

1. Translating user prompts into structured function calls (Function Calling).
2. Executing those function calls within an organized system (Model Context Protocol or MCP).

Function Calling: Bridging LLMs with External Tools

Function Calling enables LLMs to interact with external systems by invoking predefined functions based on user prompts. For example, if someone asks, "What's the weather in Chennai in Celsius?", the LLM generates a function call containing the necessary details (location and temperature unit) to retrieve the weather data. 

This mechanism allows LLMs to execute tasks beyond their inherent training data, such as fetching real-time information or performing specific computations. Each LLM provider has their own way of defining these functions. There is no universal standard yet. 

Example of a Function call from different LLM providers

1. OpenAI

{
  "index": 0,
  "message": {
    "role": "assistant",
    "content": null,
    "tool_calls": [
      {
        "name": "get_weather",
        "arguments": "{\n \"location\": \"Chennai\",\n \"unit\": \"Celsius\"\n}"
      }
    ]
  },
  "finish_reason": "tool_calls"
}

2. Claude

{
  "role": "assistant",
  "content": [
    {
      "type": "text",
      "text": "<thinking>To answer this question, I will: …</thinking>"
    },
    {
      "type": "tool_use",
      "id": "abcj8765",
      "name": "get_weather",
      "input": {"location": "Chennai", "unit": "Celsius"}
    }
  ]
}

3. Gemini

{
  "functionCall": {
    "name": "get_weather",
    "args": {
      "location": "Chennai",
      "unit": "Celsius"
    }
  }
}

4. Llama

{
  "role": "assistant",
  "content": null,
  "function_call": {
    "name": "get_weather",
    "arguments": {
      "location": "Chennai",
      "unit": "Celsius"
    }
  }
}

How Function Calling Works:

1. Function Definition: Developers define a set of functions that the LLM can access, specifying their purposes and the required parameters.
2. Prompt Interpretation: When a user inputs a prompt, the LLM determines if a function call is necessary to fulfil the request.
3. Function Invocation: The LLM generates a structured call to the appropriate function, passing the required arguments.
4. Execution and Response: The external function executes and returns the result, which the LLM then integrates into its response to the user.

Model Context Protocol (MCP): Standardizing Context Integration

Introduced by Anthropic, the Model Context Protocol (MCP) is an open standard designed to facilitate secure, two-way connections between LLMs and external data sources or tools. MCP aims to provide a unified framework for integrating diverse data sources, enabling LLMs to access and utilize external context seamlessly. 

Key Features of MCP

• Standardized Interface: MCP offers a consistent protocol for connecting LLMs with various data sources, reducing the need for custom integrations.
• Dynamic Context Provision: Allows LLMs to receive real-time updates and context from external systems, enhancing their responsiveness and accuracy.
• Scalability: Designed to handle complex, context-aware interactions at scale, making it suitable for enterprise applications.

How MCP Works

1. MCP Servers and Clients: Developers can set up MCP servers to expose their data, while AI applications act as MCP clients that connect to these servers.
2. Context Exchange: Through MCP, LLMs can request and receive contextual information from connected data sources, enabling more informed responses.
3. Secure Communication: MCP emphasizes secure data transmission, ensuring that sensitive information is protected during interactions.

Example MCP Request

{
  "jsonrpc": "2.0",
  "id": 128,
  "method": "tools/call",
  "params": {
    "name": "get_weather",
    "arguments": {
      "location": "Chennai",
      "unit": "Celsius"
    }
  }
}

When to Use Function-Calling

• When the task requires the immediate execution of well-defined operations.
• When responses need to be consistent and machine-readable (e.g., JSON format).
• If the interaction is one-shot and doesn’t require maintaining context.
• When integrating with external systems that expect structured inputs and outputs.

When to Use MCP

• When the task involves reasoning across multiple exchanges.
• Maintaining long-term memory is necessary for a coherent user experience.
• When responses need to be adaptive based on prior inputs.
• For guiding users through multi-step workflows with flexible branching.
• When balancing natural conversation with structured processing is required.

Comparative Analysis of MCP and Function Calling

Conclusion

Both Model Context Protocol (MCP) and Function calling represent significant strides in enhancing the capabilities of LLMs by integrating them with external tools and data sources.

Function Calling offers a straightforward approach for extending LLM functionalities through predefined functions, ensuring predictability and control. 

In contrast, MCP provides a standardized and scalable framework for dynamic, context-aware interactions, positioning it as a robust solution for complex enterprise applications.

The choice between implementing Function Calling or adopting MCP hinges on the specific requirements of the application, including factors such as scalability, flexibility, and the complexity of interactions needed. 

As the AI ecosystem continues to evolve, both approaches are likely to play pivotal roles in shaping the future of LLM integrations.