What is MCP in AI? The Model Context Protocol Explained

Every major AI company has released some kind of "tool use" or "function calling" feature. OpenAI has function calling. Anthropic has tool use. Google has grounding. They all solve the same problem: giving LLMs access to external data and actions. But they're all different implementations, and building a tool that works with all of them means writing separate integrations for each.

MCP (Model Context Protocol) is Anthropic's attempt to standardize this. Released as open source in late 2024, MCP defines a common protocol for connecting AI models to external tools, data sources, and services. Think of it as USB-C for AI tools -- one standard connector instead of a drawer full of proprietary cables.

Key Takeaways

• MCP is an open protocol that standardizes how AI models connect to external tools and data
• It solves the fragmentation problem where every AI provider has a different tool integration format
• MCP servers expose tools via a free JSON formatter-RPC protocol; MCP clients (LLM applications) discover and call them
• Major adoption is growing: Claude Desktop, Cursor, VS Code, Zed, Windsurf, and other platforms support MCP
• SearchHive exposes web search, scraping, and research tools via MCP servers

How MCP Works

MCP follows a client-server architecture:

• MCP Client: The LLM application (Claude Desktop, Cursor, your custom app). Discovers available tools and decides when to call them based on the model's output.
• MCP Server: Provides tools and resources. Exposes them through a standardized JSON-RPC interface. Can run locally or remotely.
• MCP Host: The application that manages connections between clients and servers.

LLM Application (MCP Client)
    |
    | JSON-RPC over stdio / HTTP / SSE
    |
MCP Server (web_search, file_system, database, ...)

When a user asks a question, the client checks which MCP servers are connected and what tools they offer. The LLM sees the available tools, decides which ones to call, and the client executes the call through the MCP protocol.

MCP Tool Interface

Tools in MCP are defined with a simple schema:

{
  "name": "web_search",
  "description": "Search the web for current information",
  "inputSchema": {
    "type": "object",
    "properties": {
      "query": {
        "type": "string",
        "description": "The search query"
      },
      "num_results": {
        "type": "number",
        "description": "Number of results to return",
        "default": 5
      }
    },
    "required": ["query"]
  }
}

When the LLM decides to call this tool, the MCP client sends a JSON-RPC request to the server:

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "web_search",
    "arguments": {
      "query": "latest web scraping trends 2026",
      "num_results": 5
    }
  }
}

The server responds with the tool result, which gets injected back into the LLM's context for further reasoning.

MCP vs. Native Tool Use

Why not just use each provider's native tool calling?

Native tool use works fine if you're building for a single LLM provider. MCP becomes valuable when you want your tools to work across Claude, GPT-4, Gemini, local models, and whatever comes next.

What MCP Servers Exist

The MCP ecosystem is growing rapidly:

Using SearchHive as an MCP Server

SearchHive exposes SwiftSearch, ScrapeForge, and DeepDive as MCP tools. This means any MCP-compatible AI application can search the web, extract data, and do deep research without custom integration code.

To configure SearchHive as an MCP server in Claude Desktop:

{
  "mcpServers": {
    "searchhive": {
      "command": "npx",
      "args": ["-y", "@searchhive/mcp-server"],
      "env": {
        "SEARCHHIVE_API_KEY": "your-api-key-here"
      }
    }
  }
}

Once configured, Claude gains access to three tools:

• swiftsearch -- Run web searches and get structured results
• scrapeforge -- Extract content from any URL
• deepdive -- Deep research across multiple sources

For programmatic use in your own application:

from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client

async def use_searchhive_mcp():
    server_params = StdioServerParameters(
        command="npx",
        args=["-y", "@searchhive/mcp-server"],
        env={"SEARCHHIVE_API_KEY": "your-api-key"}
    )

    async with stdio_client(server_params) as (read, write):
        async with ClientSession(read, write) as session:
            await session.initialize()

            # List available tools
            tools = await session.list_tools()
            for tool in tools.tools:
                print(f"Tool: {tool.name} - {tool.description}")

            # Search the web
            result = await session.call_tool(
                "swiftsearch",
                arguments={"query": "best web scraping APIs 2026"}
            )
            print(result)

Building Your Own MCP Server

MCP servers can be built in any language. Here's a minimal Python example:

from mcp.server import Server
from mcp.server.stdio import stdio_server
import json

app = Server("my-tools")

@app.list_tools()
async def list_tools():
    return [
        {
            "name": "calculate",
            "description": "Perform a mathematical calculation",
            "inputSchema": {
                "type": "object",
                "properties": {
                    "expression": {"type": "string", "description": "Math expression"}
                },
                "required": ["expression"]
            }
        }
    ]

@app.call_tool()
async def call_tool(name, arguments):
    if name == "calculate":
        try:
            result = eval(arguments["expression"])
            return [{"type": "text", "text": str(result)}]
        except Exception as e:
            return [{"type": "text", "text": f"Error: {e}"}]

async def main():
    async with stdio_server() as (read_stream, write_stream):
        await app.run(read_stream, write_stream, app.create_initialization_options())

import asyncio
asyncio.run(main())

This server exposes a calculate tool that any MCP client can discover and use.

MCP Transport Options

stdio (standard input/output) is the simplest and most common for development. The MCP server runs as a subprocess, communicating with the client through pipes. For production deployments, HTTP+SSE or the newer Streamable HTTP transport allows remote server connections.

Limitations and Current Challenges

MCP is still maturing. Be aware of:

• No built-in authentication standard. API keys are passed via environment variables, not the protocol itself.
• Limited streaming support. Long-running tool calls block until completion.
• Server management is manual. No discovery protocol for finding public MCP servers yet.
• Tool schema validation varies. Not all clients enforce the JSON Schema strictly.
• Version fragmentation. The spec is evolving, and not all implementations track the latest version.

The Road Ahead

MCP has strong momentum. Claude Desktop, Cursor, VS Code, Zed, Windsurf, and Continue all support MCP servers. The protocol is open source under a permissive license. As more tool builders adopt MCP, the "write once, run everywhere" promise gets closer to reality.

For AI agents that need web access, MCP servers like SearchHive's mean your agent can search, scrape, and research without per-provider integration code. Plug in the MCP server, and the agent has the web.

Get Started

SearchHive gives you 500 free credits. Sign up, get your API key, and configure the MCP server in Claude Desktop or your custom application. Sign up for free.

Read more about search APIs for LLMs or how AI agents browse the web.