Model Context Protocol (MCP): A New Standard for Tool and Data Integration

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Large language models are evolving fast, but their real power comes from connecting to the outside world. Enter Anthropic’s Model Context Protocol (MCP)—a universal standard that makes it easier for AI to call external tools, access real-time data, and interact fluidly with services. In this first article of our series, we unpack what MCP is, why it matters, and how it relates to existing function calling approaches.

The Evolution of Function Calling in LLMs

Before diving into MCP, let's understand the concept of function calling in LLMs. Function calling allows language models to invoke external functions or tools in response to user requests. For example, if you ask "What's Apple's stock price right now?", instead of hallucinating an answer, the LLM can recognize it needs real-time data and generate a structured call to a stock price API.

Different AI providers implement this feature differently:

  • OpenAI's API returns JSON objects with function calls
  • Anthropic's Claude outputs specially formatted tool-use blocks
  • Other models might have their own conventions

The core concept remains the same: the LLM translates a natural language request into a structured command that external systems can execute.

Traditionally, implementing these capabilities required extensive custom code. Developers had to:

  1. Parse the model's output
  2. Map it to an appropriate API call
  3. Execute the call
  4. Format the results back into the conversation

These bespoke integrations were cumbersome and didn't generalize well. Each new tool required new custom code, creating a proliferation of one-off solutions.

Enter the Model Context Protocol (MCP)

Introduced by Anthropic in late 2024, the Model Context Protocol (MCP) aims to standardize how AI models interact with external tools, data, and services. Anthropic describes MCP as a sort of "USB-C port" for AI applications—a universal, plug-and-play interface for connecting LLMs to all sorts of external resources.

MCP addresses what's known as the M×N integration problem. Without a standard, connecting M different models to N different tools requires M×N bespoke integrations. With MCP, each model and tool only needs to implement the protocol once (M+N connections total) to work with all others. This dramatically reduces development effort while increasing interoperability.

How MCP Works

At its core, MCP is built on JSON-RPC 2.0, a lightweight remote procedure call protocol that encodes requests and responses as JSON messages. However, MCP goes beyond simple RPC by defining a complete framework for AI-tool interactions.

MCP follows a client-server architecture:

  • The client side is the AI application (like a chat interface or IDE with an AI assistant)
  • The server side provides tools and capabilities (like a weather API, database access, etc.)

These components communicate by exchanging JSON-RPC messages over a channel (which could be a local pipe, WebSocket, or HTTP stream, depending on the deployment).

The protocol defines several types of "primitives" that servers can provide:

1. Tools

These are executable functions that the LLM can call to perform actions or fetch information. For example, a weather server might offer a get_forecast(location) tool that returns current weather data. This is the direct analogue of traditional function calling.

2. Resources

These are structured data sources or documents that can be retrieved and fed into the model's context. Unlike Tools (which perform actions), Resources provide information to augment the model's context. For instance, a code repository server might expose resources representing file contents.

3. Prompts

These are predefined prompt templates or instructions that can be invoked. They help standardize common instructions or persona behaviors.

On the client side, MCP defines additional primitives like Roots (controlled access to client-managed data) and Sampling (allowing tools to request model completions in a controlled way).

The MCP Interaction Flow

When an AI needs to use a tool via MCP, the sequence typically looks like this:

  1. Discovery Phase: The client and server perform a handshake and exchange information about their capabilities. The server advertises available tools, and the client shares what features it supports.
  2. Invocation: When the user asks something requiring external data, the LLM decides to call a tool. The client sends a JSON-RPC request to the server invoking that tool with appropriate parameters.
{
  "jsonrpc": "2.0", 
  "id": 123, 
  "method": "tools/call", 
  "params": {
    "name": "get_current_stock_price", 
    "arguments": {"company": "AAPL", "format": "USD"}
  }
}
  1. Execution: The server processes the request, executes the function, and returns the results (or an error if something went wrong).
  2. Result Integration: The client integrates the returned data back into the conversation, allowing the model to provide an informed response.

What makes MCP powerful is that this connection remains open and stateful. The model can invoke multiple tools in sequence during a single session, creating fluid, multi-step interactions.

MCP vs. Traditional Approaches

MCP offers several advantages over traditional API integrations or platform-specific function calling:

  • Standardization: Unlike OpenAI's function calling or ChatGPT plugins, MCP isn't tied to a single vendor. It's an open standard any AI platform can adopt.
  • Persistence: MCP connections maintain state, allowing ongoing dialogues between the model and tools, rather than just isolated one-off requests.
  • Discovery: Tools can be discovered dynamically at runtime, rather than being predefined in advance.
  • Composability: Multiple tools from different providers can work together seamlessly within the same session.

Consider this comparison:

  • Direct API integration: Requires custom parsing and formatting code for each new tool
  • OpenAI's function calling: Works only with OpenAI models and requires predefined functions
  • ChatGPT plugins: Closed ecosystem, one-off connections without a consistent protocol
  • MCP: Open standard providing persistent, discoverable, and composable tool connections for any compatible LLM

Why MCP Matters

MCP represents a significant step toward more capable AI assistants. By standardizing how LLMs interact with external systems, it makes it easier to build AI that can:

  • Access real-time information
  • Perform actions in the digital world
  • Combine multiple tools to solve complex problems
  • Maintain context across multiple interactions

In essence, MCP provides the "nervous system" connecting AI "brains" to the rest of the digital world.

In the next part of this series, we'll dive deeper into MCP's architecture, explore its unique features, and examine how it enables sophisticated multi-step reasoning and tool use in AI systems.

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