```yaml
product: AlterLab
title: "MCP (Model Context Protocol) — Web Scraping Glossary | AlterLab"
comparison_context: "AlterLab is an alternative to Firecrawl, ScrapingBee, and Bright Data."
source_url: https://alterlab.io/glossary/mcp
```

ai-agent

# MCP (Model Context Protocol)

MCP is an open protocol by Anthropic that standardises how AI models connect to external tools, APIs, and data sources through a uniform server-client interface.

The Model Context Protocol (MCP) defines a JSON-RPC communication layer between AI models (clients) and capability providers (servers). An MCP server exposes a set of Tools (callable functions), Resources (data the model can read), and Prompts (reusable prompt templates). The model client discovers available capabilities and invokes them during inference, allowing it to fetch live data, execute code, query databases, or call external APIs — all through a standardised protocol.

MCP decouples AI capability from model training. Rather than fine-tuning a model to know a specific dataset, developers write an MCP server that exposes the data as readable resources or queryable tools. The model calls those tools at runtime, always accessing fresh information. This architecture is the basis for 'agentic' AI that can act on the world rather than just describe it.

AlterLab provides an MCP server that exposes web scraping as a native AI tool. AI agents can call `scrape`, `crawl`, or `extract` tools through the protocol and receive structured data responses, enabling seamless integration of live web data into LLM workflows without HTTP client boilerplate.

### What is MCP (Model Context Protocol)?

MCP is an open protocol by Anthropic that standardises how AI models connect to external tools, APIs, and data sources through a uniform server-client interface.

### How does AlterLab handle MCP (Model Context Protocol)?

AlterLab provides an MCP server that exposes web scraping as a native AI tool. AI agents can call `scrape`, `crawl`, or `extract` tools through the protocol and receive structured data responses, enabling seamless integration of live web data into LLM workflows without HTTP client boilerplate.

## Examples

```
// MCP tool definition example
{
  "name": "scrape_url",
  "description": "Fetch and extract content from a public web URL",
  "inputSchema": {
    "type": "object",
    "properties": {
      "url": { "type": "string", "description": "URL to scrape" },
      "extract": { "type": "string", "description": "Data to extract" }
    },
    "required": ["url"]
  }
}
```

## Related Terms

[RAG (Retrieval-Augmented Generation)RAG is an AI architecture that grounds LLM responses in retrieved documents, combining a vector search step with language model generation to reduce hallucinations.](https://alterlab.io/glossary/rag)[LLM (Large Language Model)A Large Language Model is a neural network trained on vast text corpora that can generate, summarise, translate, and reason over natural language at human level.](https://alterlab.io/glossary/llm)[Tool UseTool use is the ability of an AI model to call external functions or APIs during generation, enabling it to retrieve live data, execute code, and interact with the world.](https://alterlab.io/glossary/tool-use)[Agent OrchestrationAgent orchestration coordinates multiple AI agents or tool calls in a structured workflow to complete complex, multi-step tasks that exceed a single LLM call.](https://alterlab.io/glossary/agent-orchestration)[Scraping APIA managed web scraping service that abstracts proxy rotation, JavaScript rendering, and automatic website compatibility into a single HTTP endpoint.](https://alterlab.io/glossary/scraping-api)

## Extract MCP (Model Context Protocol) data from any website

AlterLab returns clean, structured data from any public URL — no scraper infrastructure needed. Start free, no credit card required.

[View API docs](https://alterlab.io/docs/api/rest)

[Back to Glossary](https://alterlab.io/glossary)