Contacts
Case studies

Function Calling in Large Language Models (LLMs): A Comprehensive Analysis

Introduction

Large Language Models (LLMs) such as GPT-4, Claude, and PaLM have revolutionized natural language processing (NLP) by enabling machines to understand and generate human-like text. One of the most transformative features in recent LLM architectures is function calling—the ability for an LLM to recognize, structure, and execute function calls based on user intent. This innovation allows LLMs to interact with external tools, APIs, and databases, thereby extending their capabilities far beyond text generation.

This essay explores the concept of function calling in LLMs, comparing various approaches to representing function calls: plaintext (e.g., <arg1>value</arg1>), JSON, and more advanced protocols such as MCP (Message Control Protocol) and A2A (Agent-to-Agent). We will analyze their strengths, weaknesses, and suitability for different use cases, providing code examples and practical insights.

1. Understanding Function Calling in LLMs

Function calling in LLMs refers to the model's ability to:

  • Interpret user intent (e.g., "What's the weather in Paris?")
  • Map intent to a function signature (e.g., get_weather(location: str))
  • Extract and structure arguments (e.g., location = "Paris")
  • Format the function call in a way that external systems can process
  • Return and integrate results into the conversation

This process requires not only language understanding but also structured reasoning and adherence to specific data formats.

2. Plaintext Function Calling

2.1. Description

Plaintext function calling involves representing function calls and their arguments in a human-readable, often markup-like format. For example:

<arg1>Paris</arg1>
<arg2>2024-06-10</arg2>

Or, as a complete function call:

<function>get_weather</function>
<location>Paris</location>
<date>2024-06-10</date>

2.2. Advantages

  • Human-readable: Easy for humans to read and understand.
  • Simple to implement: No need for parsing complex data structures.
  • Flexible: Can be adapted for quick prototyping.

2.3. Disadvantages

  • Ambiguity: Lack of strict schema can lead to misinterpretation.
  • Parsing complexity: Requires custom parsers to extract data.
  • Error-prone: No validation against a schema; typos or missing tags can break the process.

2.4. Example

Suppose a user asks: "Book a flight from New York to London on July 1st."

The LLM might output:

<function>book_flight</function>
<from>New York</from>
<to>London</to>
<date>2024-07-01</date>

A backend system would need to parse this output, extract the values, and execute the corresponding function.

3. JSON-based Function Calling

3.1. Description

JSON (JavaScript Object Notation) is a lightweight, widely-used data interchange format. Many LLMs, including OpenAI's GPT-4, now support function calling using structured JSON outputs.

Example:

{
  "function": "book_flight",
  "arguments": {
    "from": "New York",
    "to": "London",
    "date": "2024-07-01"
  }
}

3.2. Advantages

  • Machine-readable: Easily parsed by virtually all programming languages.
  • Schema validation: Can enforce argument types and required fields.
  • Standardized: Widely adopted in APIs and data exchange.

3.3. Disadvantages

  • Less human-friendly: Not as readable as plaintext for non-technical users.
  • Verbosity: Can be more verbose than necessary for simple calls.
  • Requires strict formatting: Minor syntax errors (e.g., missing commas) can break parsing.

3.4. Example

User query: "Set a reminder for tomorrow at 9 AM."

LLM output:

{
  "function": "set_reminder",
  "arguments": {
    "time": "2024-06-11T09:00:00",
    "note": "Reminder"
  }
}

A backend can directly parse this JSON and execute the set_reminder function.

4. MCP (Message Control Protocol) and A2A (Agent-to-Agent) Approaches

4.1. Description

MCP and A2A are more advanced protocols designed for structured, multi-agent communication and orchestration. They are often used in environments where multiple agents (LLMs, tools, APIs) need to interact, coordinate, or delegate tasks.

MCP Example

MCP messages often use a standardized envelope with metadata, sender/receiver IDs, and payloads.

{
  "protocol": "MCP",
  "message_id": "abc123",
  "sender": "LLM_Agent_1",
  "receiver": "FlightBookingService",
  "timestamp": "2024-06-10T15:00:00Z",
  "payload": {
    "function": "book_flight",
    "arguments": {
      "from": "New York",
      "to": "London",
      "date": "2024-07-01"
    }
  }
}

A2A Example

A2A protocols may include additional context, such as conversation history, intent, or multi-step workflows.

{
  "protocol": "A2A",
  "conversation_id": "conv456",
  "step": 3,
  "intent": "BookFlight",
  "agent": "LLM_Agent_1",
  "target_agent": "FlightBookingService",
  "parameters": {
    "from": "New York",
    "to": "London",
    "date": "2024-07-01"
  },
  "context": {
    "previous_steps": [
      {"step": 1, "action": "AskUser", "result": "User wants to book a flight"},
      {"step": 2, "action": "GetDetails", "result": "From New York to London"}
    ]
  }
}

4.2. Advantages

  • Rich metadata: Supports complex workflows, multi-agent orchestration, and traceability.
  • Scalability: Suitable for large systems with many interacting components.
  • Extensibility: Can add new fields (e.g., security, logging) as needed.

4.3. Disadvantages

  • Complexity: More difficult to implement and maintain.
  • Overhead: Additional metadata increases message size.
  • Requires strict adherence: All agents must conform to protocol specifications.

5. Comparative Analysis

Feature Plaintext (<arg1>value</arg1>) JSON MCP/A2A
Human Readability High Medium Low
Machine Readability Low/Medium (needs parsing) High High
Schema Validation Low High High
Extensibility Low Medium High
Complexity Low Medium High
Use Case Prototyping, simple apps Production APIs, LLM tools Multi-agent, orchestration

5.1. When to Use Each Approach

  • Plaintext: Best for rapid prototyping, demos, or when human readability is paramount.
  • JSON: Ideal for production systems, APIs, and when integrating with modern LLMs that support structured outputs.
  • MCP/A2A: Necessary for complex, multi-agent systems where traceability, metadata, and orchestration are required.

6. Practical Considerations

6.1. LLM Prompt Engineering

How you prompt the LLM greatly influences the output format. For example, to encourage JSON output:

You are a function-calling assistant. When asked a question, respond with a JSON object specifying the function and its arguments.

For plaintext:

Respond with function arguments in the format: <arg1>value</arg1>

6.2. Error Handling

  • Plaintext: Errors are harder to detect; missing tags or malformed text may go unnoticed.
  • JSON: Parsers can catch syntax errors, but LLMs may still hallucinate invalid JSON.
  • MCP/A2A: Protocols often include error fields and status codes for robust handling.

6.3. Security

  • Plaintext: Susceptible to injection attacks or misinterpretation.
  • JSON: Can implement validation and sanitization.
  • MCP/A2A: Can include authentication, authorization, and encryption fields.

7. Code Examples

7.1. Parsing Plaintext in Python

import re

def parse_plaintext(text):
    pattern = r"<(\w+)>(.*?)</\1>"
    return {match[0]: match[1] for match in re.findall(pattern, text)}

text = "<function>book_flight</function><from>New York</from><to>London</to><date>2024-07-01</date>"
print(parse_plaintext(text))
# Output: {'function': 'book_flight', 'from': 'New York', 'to': 'London', 'date': '2024-07-01'}

7.2. Parsing JSON in Python

import json

def parse_json(json_str):
    return json.loads(json_str)

json_str = '''
{
  "function": "book_flight",
  "arguments": {
    "from": "New York",
    "to": "London",
    "date": "2024-07-01"
  }
}
'''
print(parse_json(json_str))

7.3. Handling MCP/A2A Messages

def handle_mcp_message(message):
    payload = message.get("payload", {})
    function = payload.get("function")
    arguments = payload.get("arguments", {})
    # Execute function based on extracted data
    # ...

mcp_message = {
    "protocol": "MCP",
    "message_id": "abc123",
    "sender": "LLM_Agent_1",
    "receiver": "FlightBookingService",
    "timestamp": "2024-06-10T15:00:00Z",
    "payload": {
        "function": "book_flight",
        "arguments": {
            "from": "New York",
            "to": "London",
            "date": "2024-07-01"
        }
    }
}
handle_mcp_message(mcp_message)

8. Future Directions

As LLMs become more deeply integrated into software systems, function calling will continue to evolve. Key trends include:

  • Standardization: Emergence of universal schemas and protocols for LLM function calling.
  • Tool Use: LLMs autonomously selecting and invoking external tools.
  • Multi-agent Collaboration: LLMs coordinating with other agents, APIs, and services.
  • Security and Governance: Enhanced controls for authentication, authorization, and auditing.

Conclusion

Function calling in LLMs marks a significant leap forward in AI capabilities, enabling models to interact with the world in structured, programmable ways. The choice of representation—plaintext, JSON, or advanced protocols like MCP/A2A—depends on the specific requirements of the application, balancing human readability, machine parsing, extensibility, and complexity.

  • Plaintext is best for simple, human-centric tasks.
  • JSON is the current standard for robust, machine-to-machine communication.
  • MCP/A2A protocols are essential for orchestrating complex, multi-agent workflows.

As the ecosystem matures, we can expect further innovation in how LLMs represent, execute, and manage function calls, unlocking new possibilities for intelligent automation and collaboration.