The phrase “USB-C for AI” is often used to describe the Model Context Protocol (MCP), and it’s an apt comparison. But what exactly is MCP and why is it so significant for the future of artificial intelligence?

At their core, large language models (LLMs) possess vast knowledge, but their understanding is limited to their training data. Without external connections, they can’t access real-time information or integrate with specialized software. This is where MCP steps in.

MCP establishes a universal communication framework, allowing LLMs like GPT or Claude to seamlessly interact with any compatible tool or service. Rather than relying on bespoke APIs or fragmented integrations, MCP introduces a standardized language, enabling intelligent agents (clients) to communicate effectively with software applications (servers).

How MCP Facilitates AI Interaction

The operational principle behind MCP is straightforward: a client-server architecture. The large language model functions as the client, while a server hosts and provides access to various tools that the AI can utilize. This communication is orchestrated via JSON-RPC.

The process begins with the AI and server initiating a negotiation of their respective capabilities. Subsequently, the AI client dispatches a tools/list request to the server, similar to this:

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/list",
  "params": {
    "cursor": "optional-cursor-value"
  }
}

In response, the server furnishes a comprehensive manifest detailing the available tools, like this example for a weather tool:

{
  "jsonrpc": "2.0",
  "id": 1,
  "result": {
    "tools": [
      {
        "name": "get_weather",
        "description": "Get current weather information for a location",
        "inputSchema": {
          "type": "object",
          "properties": {
            "location": {
              "type": "string",
              "description": "City name or zip code"
            }
          },
          "required": ["location"]
        }
      }
    ],
    "nextCursor": "next-page-cursor"
  }
}

Equipped with knowledge of the available tools, the AI can then select the appropriate one to fulfill a user’s request. For instance, to check the weather in Lisbon, the AI would invoke the get_weather tool as follows:

{
  "jsonrpc": "2.0",
  "id": 2,
  "method": "tools/call",
  "params": {
    "name": "get_weather",
    "arguments": {
      "location": "Lisbon"
    }
  }
}

The MCP server then delivers a structured response, providing the requested information—in this case, the weather conditions in Lisbon:

{
  "jsonrpc": "2.0",
  "id": 2,
  "result": {
    "content": [
      {
        "type": "text",
        "text": "Current weather in Lisbon:
Temperature: 32°C
Conditions: Partly cloudy"
      }
    ],
    "isError": false
  }
}

Local Versus Remote MCP Server Deployments

Implementing an MCP server can be done in a couple of ways. The simplest is to host it on the same machine as the client. For applications like OpenAI Codex or Claude Desktop, the AI can initiate an MCP server locally and communicate through standard input/output (stdio).

For more elaborate setups, MCP supports communication over HTTP and incorporates robust mechanisms for authentication and authorization. These remote servers can demand credentials, API keys, or tokens, depending on the sensitivity of the services they offer.

The Evolving MCP Standard

The Model Context Protocol is a relatively new but rapidly developing open standard. Launched in 2024, it’s being collaboratively advanced by a diverse group within the AI community.

The foundational specification is available at modelcontextprotocol.info, with ongoing development fostering contributions from leading AI companies, open-source contributors, and infrastructure providers.

The Transformative Impact of MCP

MCP signifies a subtle yet profound evolution in how AI systems engage with the digital world. It provides a standardized, open framework – a common dialect that enables any AI model and any software tool to interact seamlessly.

For developers, this translates into a reduced need for custom, one-off integrations and a greater emphasis on building reusable, interoperable systems. For end-users, it means more capable AI assistants that can transcend their inherent training data, accessing real-time information, files, and applications with enhanced accuracy and contextual awareness.

This paves the way for a new era of intelligent automation and sophisticated AI-driven functionalities.