Short answer: An AI-ready web crawler is a crawler whose output is shaped for LLM and agent consumption — clean markdown by default, optional structured JSON extraction via a schema or prompt, chunking-friendly metadata, an MCP transport for agent tool-use, and latency low enough that an agent loop does not stall waiting on a fetch.
Traditional crawlers (Scrapy, Heritrix, Common Crawl tooling) return raw HTML and let you write XPath or BeautifulSoup selectors. That works for fixed-schema scraping; it is the wrong shape for an LLM, which wants prose, headings, and lists — not <div class="content">. An AI-ready crawler does the readability extraction, boilerplate removal, and Markdown conversion server-side so the agent receives a few kilobytes of clean text instead of a few hundred kilobytes of nav, footer, and ad markup.
First, clean output formats: markdown, plainText, and structured JSON, not raw HTML by default. Second, LLM extraction modes: a schema (JSON Schema) or a free-text prompt that the crawler resolves into typed JSON before returning. Third, MCP support: the Model Context Protocol exposes crawl/scrape/search as tools an agent can call directly. Fourth, low p50 latency: an agent loop with a 2-second median tool call feels different from one with a 200-ms median, even if both succeed.
fastCRW returns markdown by default on every /v1/scrape call; structured extraction is exposed via /v1/extract with either a jsonSchema or a natural-language prompt (BYOK: OpenAI, Anthropic, DeepSeek). A streamable MCP transport at /mcp exposes scrape, crawl, map, and search as agent tools. p50 latency is 1914 ms on Firecrawl's public 1,000-URL dataset (diagnose_3way.py, 2026-05-08) — fastCRW carries the median speed win against Crawl4AI and Firecrawl in that head-to-head.