What is MCP in AI? — Complete Answer

MCP (Model Context Protocol) is an open standard created by Anthropic that lets AI models connect to external tools, data sources, and services through a standardized interface. Think of it as USB-C for AI -- any model can plug into any tool without custom integration code.

Released in late 2024, MCP has quickly become the de facto way to give LLMs access to real-time data, APIs, databases, and web services. Instead of baking tool access into each model separately, MCP provides a universal protocol that works across Claude, GPT, Gemini, Llama, and any other model.

Key Takeaways

• MCP is a protocol, not a product. It defines how AI models communicate with external tools and data sources.
• It solves the "walled garden" problem. Before MCP, every AI platform had its own plugin/integration system. MCP makes tools portable across models.
• MCP has three components: the host (your AI app), the client (built into the model), and the server (the tool/data provider).
• It works for web scraping, search, databases, APIs, file systems, and more.
• SearchHive's APIs can be wrapped as MCP servers, giving any MCP-compatible model instant access to web search and scraping.

How MCP Works: The Architecture

MCP follows a client-server architecture:

1. MCP Host -- The application running the AI model (e.g., Claude Desktop, a chat app, or an agent framework)
2. MCP Client -- Maintains 1:1 connections with MCP servers, built into the host
3. MCP Server -- Lightweight programs that expose specific capabilities (tools, resources, prompts) to the client

When you ask an AI model to search the web or scrape a page, the MCP client translates that request into a standardized message, sends it to the appropriate MCP server, gets the result back, and passes it to the model.

The protocol uses free JSON formatter-RPC 2.0 for communication, which means it's lightweight, well-understood, and easy to implement in any language.

What Can MCP Connect To?

MCP servers can expose three types of capabilities:

• Tools -- Functions the model can call (e.g., "search the web," "query a database," "create a Jira ticket")
• Resources -- Data the model can read (e.g., files, database records, API responses)
• Prompts -- Reusable prompt templates the model can use

In practice, this means an MCP server for web scraping might expose a scrape_url tool, while a search MCP server exposes a web_search tool. The model decides which to call based on the user's request.

Why MCP Matters for Web Search and Scraping

MCP is particularly relevant for developers building AI agents that need to interact with the web. Here's why:

• No more custom API wrappers. Before MCP, connecting an AI model to a search API meant writing model-specific integration code. Now you write one MCP server and any model can use it.
• Standardized tool discovery. MCP servers advertise their capabilities, so models automatically know what tools are available.
• Better agent architectures. Frameworks like LangChain, AutoGen, and CrewAI are adding MCP support, making it the standard way to give agents tools.

Using SearchHive as an MCP Server

SearchHive's web search and scraping APIs are a natural fit for MCP. Here's how to wrap them:

# mcp_searchhive_server.py -- SearchHive MCP server
from mcp.server import Server
from mcp.types import Tool, TextContent
import httpx

server = Server("searchhive")

SEARCHHIVE_API_KEY = "your-api-key"

@server.list_tools()
async def list_tools():
    return [
        Tool(name="swift_search", description="Search the web using SearchHive SwiftSearch API",
             inputSchema={"type": "object", "properties": {"query": {"type": "string"}}, "required": ["query"]}),
        Tool(name="scrape_forge", description="Scrape a webpage using SearchHive ScrapeForge API",
             inputSchema={"type": "object", "properties": {"url": {"type": "string"}}, "required": ["url"]}),
    ]

@server.call_tool()
async def call_tool(name, arguments):
    headers = {"Authorization": f"Bearer {SEARCHHIVE_API_KEY}"}
    if name == "swift_search":
        resp = httpx.get("https://api.searchhive.dev/v1/search", headers=headers, params={"query": arguments["query"]})
        return [TextContent(type="text", text=str(resp.json()))]
    elif name == "scrape_forge":
        resp = httpx.get("https://api.searchhive.dev/v1/scrape", headers=headers, params={"url": arguments["url"]})
        return [TextContent(type="text", text=str(resp.json()))]

This gives any MCP-compatible AI model instant access to web search and page scraping through SearchHive's infrastructure.

MCP vs Other Integration Approaches

The key advantage: write your integration once as an MCP server, and it works with every AI model and framework that supports the protocol.

Which AI Platforms Support MCP?

• Claude Desktop -- Native support, the reference implementation
• Cursor -- MCP support for code-aware AI
• Windsurf -- IDE with built-in MCP client
• LangChain -- MCP adapter available
• AutoGen -- MCP support added
• OpenAI -- Community MCP servers available
• Local models (Ollama, LM Studio) -- MCP clients available

The ecosystem is growing weekly. Anthropic open-sourced the MCP specification, and adoption has been rapid across both proprietary and open-source AI platforms.

FAQ

Is MCP free to use? Yes. The MCP specification is open-source (Apache 2.0). You don't pay anything to use the protocol. You only pay for the underlying tools and APIs your MCP servers connect to, like SearchHive's web search and scraping APIs.

Can I use MCP with local/open-source models? Yes. MCP is model-agnostic. You can use it with Llama, Mistral, Qwen, or any model running locally through Ollama, LM Studio, or similar tools. The MCP client runs in your application, not in the model itself.

Does MCP work for real-time web search? Yes, and that's one of its strongest use cases. Wrapping a search API like SearchHive's SwiftSearch as an MCP server gives any AI model the ability to search the web in real time. /blog/what-is-a-serp-api-complete-answer

What's the difference between MCP and function calling? Function calling is a model capability (the model knows how to call functions). MCP is a protocol for connecting models to tools. They complement each other -- MCP provides the infrastructure, function calling provides the model-side mechanism to use it.

Is MCP only for Anthropic/Claude? No. While Anthropic created MCP, it's an open standard. Any AI platform can implement MCP clients and servers. Claude Desktop was just the first to ship native support.

Can MCP servers run remotely? Yes. MCP supports both local servers (running on your machine) and remote servers (accessible via HTTP/SSE). Remote servers are ideal for centralized tools like search APIs and scraping services.

How does MCP handle authentication? MCP itself doesn't define authentication -- that's handled by the transport layer. Local servers typically don't need auth. Remote servers can use OAuth, API keys, or any standard authentication mechanism.

Is MCP production-ready? Yes. Anthropic released MCP 1.0 in 2025, and it's being used in production by companies building AI agents. The protocol is stable, well-documented, and has multiple reference implementations.

Getting Started with MCP + SearchHive

The fastest way to start using MCP is to connect it to SearchHive's APIs. SearchHive offers a free tier with 500 credits -- enough to build and test your MCP integration before scaling.

Steps:

1. Get a free API key at searchhive.dev/pricing
2. Install an MCP client (Claude Desktop, or the MCP Python SDK)
3. Set up SearchHive's SwiftSearch and ScrapeForge as MCP servers
4. Start asking your AI model to search and scrape the web

The combination of MCP's standardized protocol and SearchHive's comprehensive web data APIs gives you a production-ready setup for any AI agent that needs to interact with the web.

/compare/firecrawl | /blog/what-is-a-serp-api-complete-answer

Ready to give your AI models web access? Start with 500 free credits at SearchHive -- no credit card required. Our SwiftSearch, ScrapeForge, and DeepDive APIs are designed to work with MCP out of the box.