Model Context Protocol (MCP) is an open standard, introduced by Anthropic in November 2024, that lets an AI agent discover and call external tools — a web search, a database query, a scraping endpoint — through one consistent interface instead of a bespoke integration per data source. It has become the default tool-calling protocol most agent frameworks support by 2026.
An MCP server can expose three kinds of primitives: tools (functions the model can call, each with a typed input schema and a predictable output, like a search or scrape action), resources (read-only data the model can reference), and prompts (reusable templates). An MCP client — Claude Desktop, Cursor, Cline, Windsurf, or a custom agent — connects to the server, discovers which tools it offers by reading their schemas, and calls them mid-task with the arguments the schema calls for. The model never needs hand-written glue code per source; it reads the tool's description and shape, then calls it like any other function.
The practical payoff is that adding a new data source, or swapping one search engine for another, is a server-side change rather than an agent rewrite — the same MCP connection keeps working across every client that speaks the protocol, which is why LangChain, CrewAI, LangGraph, and LlamaIndex all moved MCP from experimental support to their default tool-calling path during 2025-2026.
Most LLMs are frozen at a training cutoff and can't see the live web on their own. Before MCP, giving an agent current data meant writing a scraper or API client per source, then maintaining the proxies, headless browsers, and parsing logic that scraper needed to keep working — exactly the maintenance burden teams don't want to own just to let a model check today's price or read a current page. An MCP server built on top of a scraping or data API turns that stack into a handful of documented tool calls: the agent asks for a search, a product page, or a map listing, and gets normalized JSON back instead of raw HTML it has to parse itself.
This is also why MCP servers for web data specifically have proliferated — vendors from Apify to Oxylabs to Zyte now ship one alongside their existing scraping API, because an agent-facing tool interface has become table stakes for the same data a developer used to reach only through a REST endpoint.
How Crawlora handles this
Crawlora runs exactly this kind of hosted MCP server at `https://mcp.crawlora.net/mcp` — one Streamable HTTP connection exposes 319 tools across 33 platforms, backed by 393 REST endpoints underneath (search, maps, e-commerce, social, finance, and more), authenticated with the same API key as the REST API and billed only on a successful response, with a free tier of 2,000 credits a month. An agent in Claude, Cursor, or any MCP client gets the same proxy-routed, browser-rendered data Crawlora's REST API returns, just reachable as a tool call instead of an HTTP request.
Glossary
FAQ
No — MCP is a protocol layer for how an agent discovers and calls tools, typically built on top of an existing API rather than replacing it. Crawlora's MCP server and REST API both sit on the same underlying endpoints.
Not necessarily — a hosted MCP endpoint like Crawlora's lets you connect a client directly without hosting or maintaining a server yourself. Self-hosting only matters for a fully custom or on-prem setup.
Claude Desktop and Claude Code, Cursor, Cline, and Windsurf, plus major agent frameworks like LangChain, CrewAI, LangGraph, and LlamaIndex, all support MCP as of 2026, with more clients adding support regularly.
Beyond Model Context Protocol (MCP), Crawlora's own docs cover the rest of the stack — browse the APIs, test a request in Playground, and move from scraping infrastructure work to production data workflows.