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.