Function-Calling vs. Model Context Protocol (MCP)

Integrating Large Language Models (LLMs) with enterprise systems is transforming how businesses interact with their tools and workflows. By enabling natural language (NL) inputs to control complex operations, LLMs simplify access to enterprise capabilities, making them more intuitive and efficient. However, this integration requires a well-structured approach to handle both the translation of prompts into actionable instructions and the execution of those instructions. This is where function-calling and the Model Context Protocol (MCP) come into play, each serving a distinct yet complementary purpose.

Note: Throughout this article, the terms “function” and “tool” are used interchangeably. While MCP refers to tools within its framework, for the sake of clarity and consistency, a tool can be understood as any function or API that performs a specific task in response to an LLM-generated instruction.

The Two Phases of LLM Integration

LLMs bridge natural language inputs and enterprise systems through a two-phase process:

- Phase 1: Breaking Prompts into Function Call Instructions
Function-calling is responsible for converting natural language prompts into structured function call instructions that tool-oriented systems, such as an MCP server, can understand and act upon. This phase is focused on the generation of precise directives for tools or APIs.
- Phase 2: Executing Function Call Instructions
MCP handles the execution of these instructions by managing tool discovery, invocation, and response handling in a standardized framework. This ensures that the generated function calls are executed consistently and effectively across diverse enterprise systems.

By dividing responsibilities in this way, LLMs can integrate with a wide range of tools, from CRMs and ERPs to workflow automation systems.

Phase 1: Function-Calling — Generating Function Call Instructions

Function-calling is the mechanism by which LLMs translate user prompts into actionable instructions. For example, if a user asks, “What’s Apple’s current stock price in USD?” the LLM generates a function call specifying the desired action (e.g., fetching stock data) and the required parameters (e.g., company name and currency format).

Examples of Function-Calling Formats

There is currently no standard format for function call instructions, and each LLM vendor has its own approach. Here are some examples:

OpenAI

{
  "index": 0,
  "message": {
    "role": "assistant",
    "content": null,
    "tool_calls": [
      {
        "name": "get_current_stock_price",
        "arguments": "{\n  \"company\": \"AAPL",\n  \"format\": \"USD\"\n}"
      }
    ]
  },
  "finish_reason": "tool_calls"
}

Claude

{
  "role": "assistant",
  "content": [
    {
      "type": "text",
      "text": "<thinking>To answer this question, I will: …</thinking>"
    },
    {
      "type": "tool_use",
      "id": "toolu_01A09q90qw90lq917835lq9",
      "name": "get_current_stock_price",
      "input": {"company": "AAPL", "format": "USD"}
    }
  ]
}

Gemini

{
  "functionCall": {
    "name": "get_current_stock_price",
    "args": {
        "company": "AAPL",
        "format": "USD"
    }
  }
}
Llama

{
  "role": "assistant",
  "content": null,
  "function_call": {
      "name": "get_current_stock_price",
      "arguments": {
          "company": "AAPL",
          "format": "USD"
      }
   }
}

These examples highlight how LLMs, despite working conceptually the same, use different JSON structures to represent function calls. While a standard for function-calling could reduce this variation, current frameworks like LangChain provide abstractions to handle these differences effectively.

Phase 2: MCP — Standardized Execution of Function Call Instructions

Once the LLM generates function call instructions, they must be executed to deliver results. This is where MCP comes in. MCP provides a standardized framework for managing the execution process, including tool discovery, invocation, and response handling.

MCP’s Role in Execution

MCP enables tools to operate in a consistent and scalable manner, bridging the gap between LLM-generated instructions and enterprise systems. To achieve this, MCP uses its own request format, which requires applications to convert the LLM’s output into an MCP-compatible structure. Here’s an example of the MCP format:

MCP Request Format

{
  "jsonrpc": "2.0",
  "id": 129
  "method": "tools/call",
  "params": {
    "name": "get_current_stock_price",
    "arguments": {
      "company": "AAPL",
      "format": "USD"
    }
  },
}

Applications act as intermediaries, translating the LLM’s output into MCP-compatible requests. MCP then ensures these requests are executed correctly by the appropriate tools, returning results that the LLM can use to respond to the user.

Function-Calling vs. MCP: A Complementary Relationship

While both function-calling and MCP are integral to bridging LLMs to enterprise systems, they address different challenges:

• Function-calling focuses on translating prompts into actionable instructions. It is LLM-driven and varies across vendors, with no universal standard yet.
• MCP standardizes the execution of those instructions, enabling scalability and interoperability across thousands of tools.

The two phases together ensure that LLMs can not only interpret natural language prompts but also deliver meaningful results by leveraging enterprise tools.

Conclusion

Function-calling and MCP play distinct yet essential roles in bridging LLMs to enterprise systems. Function-calling translates prompts into actionable instructions, while MCP ensures those instructions are executed reliably and at scale. Together, they provide the foundation for natural language-driven enterprise systems, empowering businesses to unlock the full potential of LLMs in their workflows. As MCP continues to evolve, it will redefine how LLMs interact with enterprise tools, making integrations more scalable, flexible, and efficient.