Understanding AI Protocols

You've likely used AI systems like ChatGPT or Claude, but have you ever wondered why they're limited to just conversation? Why can't they take meaningful actions in the real world? The fundamental limitation of traditional Large Language Models (LLMs) has been their inability to interact with the outside world - they can generate impressive text but can't directly impact their environment.

This is where three revolutionary protocols are changing everything:

Through these three technologies, AI systems are evolving from mere "smart chatbots" into genuine digital assistants capable of taking meaningful action in the world. These protocols represent the critical evolution from isolated models to interconnected, capable assistants that can understand, access information, and collaborate to solve complex problems.

Basic Concept: What is Function Calling?

Function calling is the ability for an AI model to recognize when it needs to use an external tool or API, and to generate the proper structured arguments for that function call. It's essentially teaching AI when and how to use tools to accomplish tasks beyond simple text generation.

How Function Calling Works

┌─────────────┐      ┌─────────────┐      ┌─────────────┐
│             │      │             │      │             │
│    User     │─────▶│  AI Model   │─────▶│  Function   │
│             │ asks │             │ calls│             │
└─────────────┘      └─────────────┘      └─────────────┘
                           │                     │
                           │                     │
                           ▼                     ▼
                     "I need to use      function_name()
                      the weather         parameters
                      function"
                                              │
                                              │
                                              ▼
                                         ┌─────────────┐
                                         │             │
                                         │   Result    │
                                         │             │
                                         └─────────────┘
                                              │
                                              │
                                              ▼
                                         ┌─────────────┐
                                         │             │
                                         │ AI Response │
                                         │             │
                                         └─────────────┘
                            

Technical Implementation: Function Definition

Functions are defined in JSON Schema format, providing the AI with a clear structure of available tools:

{
  "name": "get_weather",
  "description": "Get current weather for a specific location",
  "parameters": {
    "type": "object",
    "properties": {
      "location": {
        "type": "string",
        "description": "City name (e.g., London, New York, Tokyo)"
      },
      "unit": {
        "type": "string",
        "enum": ["celsius", "fahrenheit"],
        "description": "Temperature unit"
      }
    },
    "required": ["location"]
  }
}

Function Call Output

When the AI determines a function call is needed, it generates a structured output like this:

{
  "function": "get_weather",
  "parameters": {
    "location": "London",
    "unit": "celsius"
  }
}

Advantages and Limitations

Real-World Applications

Basic Concept: What is MCP?

The Model Context Protocol (MCP) is an open standard designed to connect AI systems with various data sources in a standardized way. Developed by Anthropic in November 2024, MCP creates a universal interface for AI systems to access content repositories, business tools, and development environments without custom integrations for each source.

How MCP Works

┌──────────────┐       ┌──────────────┐
│              │       │              │
│   AI Model   │◄─────►│  MCP Client  │
│ (Claude, etc)│       │              │
└──────────────┘       └──────────────┘
                             │
                             │ MCP Protocol
                             ▼
          ┌─────────────────────────────────┐
          │                                 │
          │          MCP Servers            │
          │                                 │
          └─────────────────────────────────┘
               │         │         │
               │         │         │
               ▼         ▼         ▼
       ┌─────────┐ ┌─────────┐ ┌─────────┐
       │         │ │         │ │         │
       │ Google  │ │ GitHub  │ │ Custom  │
       │ Drive   │ │ Server  │ │ Database│
       │         │ │         │ │         │
       └─────────┘ └─────────┘ └─────────┘

Technical Implementation

The communication between MCP servers and clients follows a standardized HTTP-based protocol. Here's how it works in practice:

POST /v1/service/files/list HTTP/1.1
Content-Type: application/json

{
  "path": "/documents/project-alpha",
  "recursive": true,
  "include_content": false
}

HTTP/1.1 200 OK
Content-Type: application/json

{
  "files": [
    {
      "name": "requirements.txt",
      "path": "/documents/project-alpha/requirements.txt",
      "type": "file",
      "size": 1245,
      "last_modified": "2023-09-15T14:32:10Z"
    },
    {
      "name": "README.md",
      "path": "/documents/project-alpha/README.md",
      "type": "file",
      "size": 8732,
      "last_modified": "2023-09-10T09:15:22Z"
    }
  ]
}

Advantages and Limitations

Real-World Applications

Basic Concept: What is A2A?

The Agent-to-Agent (A2A) protocol is an open standard developed by Google in April 2025 that enables communication between different AI agents, regardless of which framework or vendor created them. A2A facilitates secure, standardized communication between independent AI systems, allowing them to collaborate on complex tasks.

How A2A Works

┌───────────────┐                   ┌───────────────┐
│               │                   │               │
│   Agent A     │                   │   Agent B     │
│  (LangChain)  │                   │  (CrewAI)     │
│               │                   │               │
└───────┬───────┘                   └───────┬───────┘
        │                                   │
        │                                   │
        │                                   │
        │                                   │
        ▼                                   ▼
┌───────────────┐                   ┌───────────────┐
│               │      A2A          │               │
│  A2A Client   │◄────Protocol─────►│  A2A Server   │
│               │                   │               │
└───────────────┘                   └───────────────┘
                                           ▲
                                           │
                                           │
                                           │
                                    ┌──────┴──────┐
                                    │             │
                                    │ Agent Card  │
                                    │             │
                                    └─────────────┘

Technical Implementation

The technical implementation of A2A involves several key components that work together to enable seamless agent communication:

{
  "name": "Travel Planning Agent",
  "version": "1.0.0",
  "description": "An agent that helps plan travel itineraries",
  "endpoint": "https://travel-agent.example.com/a2a",
  "capabilities": ["streaming", "pushNotifications"],
  "auth": {
    "type": "bearer"
  },
  "skills": [
    {
      "name": "flight_search",
      "description": "Search for flights between airports on specific dates"
    },
    {
      "name": "hotel_booking",
      "description": "Find and book hotels in various destinations"
    }
  ]
}

POST /a2a/tasks/send HTTP/1.1
Content-Type: application/json

{
  "taskId": "task_12345",
  "message": {
    "role": "user",
    "parts": [
      {
        "text": "I need to plan a trip to Goa next month"
      }
    ]
  }
}

HTTP/1.1 200 OK
Content-Type: application/json

{
  "task": {
    "taskId": "task_12345",
    "status": "working",
    "messages": [
      {
        "role": "user",
        "parts": [
          {
            "text": "I need to plan a trip to Goa next month"
          }
        ]
      },
      {
        "role": "agent",
        "parts": [
          {
            "text": "I'll help you plan your trip to Goa! To get started, could you please provide:"
          },
          {
            "data": {
              "form": {
                "fields": [
                  {"name": "travel_dates", "label": "When exactly are you planning to travel?"},
                  {"name": "budget", "label": "What's your approximate budget?"},
                  {"name": "interests", "label": "What activities are you interested in?"}
                ]
              }
            }
          }
        ]
      }
    ]
  }
}

Advantages and Limitations

Real-World Applications

Evolution Timeline

How They Work Together

These three technologies are complementary rather than competitive - they don't replace each other but instead work together. In fact, a sophisticated AI system might leverage all three protocols simultaneously to achieve maximum capability:

┌───────────────────────────────────────────────────────────┐
│                      User Request                          │
└───────────────────────┬───────────────────────────────────┘
                        │
                        ▼
┌───────────────────────────────────────────────────────────┐
│                     Primary Agent                          │
│                                                           │
│  ┌─────────────────┐    ┌────────────────┐                │
│  │ Function Calling│    │      MCP       │                │
│  │   (Tool Use)    │    │  (Data Access) │                │
│  └─────────────────┘    └────────────────┘                │
└──────────────────────────┬────────────────────────────────┘
                           │
                           │ A2A Protocol
                           ▼
┌─────────────────┐   ┌─────────────────┐   ┌─────────────────┐
│                 │   │                 │   │                 │
│  Specialist     │   │  Specialist     │   │  Specialist     │
│  Agent A        │   │  Agent B        │   │  Agent C        │
│                 │   │                 │   │                 │
└─────────────────┘   └─────────────────┘   └─────────────────┘

Advanced Use Case: Integrating All Three