Agent Protocol (MCP): The TCP/IP of AI

For the last two years, every AI company built their own "Plugin" system. OpenAI had Plugins. Anthropic had Tool Use. LangChain had Tools. It was a fragmented mess. Enter the Model Context Protocol (MCP): an open standard (pioneered by Anthropic and others) to solve the "N x M" problem of connecting Models to Data.

1. The Problem: The Connector Spaghetti

• Models: Claude, GPT-4, Llama-3...
• Data Sources: Google Drive, Slack, GitHub, Postgres...

Without a standard, you need to write a specific integration for "Claude to Slack", "GPT-4 to Slack", "Llama to Slack". That is N * M integrations.

2. The Solution: Universal Servers

MCP introduces a client-host-server architecture.

graph LR
    subgraph ClientGroup ["AI Client (Host)"]
        Claude["Claude Desktop / IDE"]
        GPT["ChatGPT / Custom App"]
    end
    
    subgraph ProtoGroup ["MCP Protocol"]
        Conn["MCP Connection (Generic)"]
    end
    
    subgraph ServerGroup ["MCP Servers"]
        Github["GitHub MCP Server"]
        Postgres["Postgres MCP Server"]
        Slack["Slack MCP Server"]
    end
    
    Claude -- "List Tools" --> Conn
    Conn --> Github
    Conn --> Postgres

How it works:

1. The MCP Server (e.g., for GitHub) exposes resources (repos, files) and tools (open_issue, create_pr).
2. The Host (the AI) connects and asks "What can you do?"
3. The Server replies with a JSON Schema.
4. The AI can now interact with GitHub without knowing specifically how GitHub works.

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3. Why Developers Love This

• Write Once, Run Anywhere: You write a "SQL Database MCP Server" once. Now Claude, ChatGPT, and your Custom Agent can ALL query the database.
• Security: The Server controls the permissions. You can run the server locally on your laptop, giving the AI access to local files without uploading them to the cloud.

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4. Code Example: A Simple MCP Server

Using the Python SDK, creating a tool is trivial.

from fastmcp import FastMCP

mcp = FastMCP("Weather Service")

@mcp.tool()
def get_weather(city: str) -> str:
    """Get the current weather for a city."""
    # Fake API call
    return f"The weather in {city} is Sunny, 25C."

if __name__ == "__main__":
    mcp.run()

That's it. Any MCP-compliant AI Client can now "see" and "use" this tool.

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5. The Future Ecosystem

We are moving towards an "App Store" for Agents, but instead of Apps, we download Capabilities.

• "I want my agent to design PCBs." -> Download the KiCad MCP Server.
• "I want my agent to trade crypto." -> Download the Coinbase MCP Server.

MCP is the plumbing that will allow the Agentic Internet to scale.