Firecrawl
For FirecrawlAI Platform

Map Url Discovery

Firecrawl · Firecrawl

Web Data for AI — Firecrawl

Evaluates Firecrawl's Map (URL discovery) across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Web Data for AI eval coverage.

About Firecrawl

Firecrawl is a web-data API for AI — it turns websites into clean, LLM-ready markdown or structured data via scrape, crawl, map, search, and LLM-powered extract endpoints, with JS rendering, browser actions, and proxies. Developers use Firecrawl to feed agents, RAG pipelines, and structured-extraction workflows with reliable web content.

Employees

~30

Industry

Web Data / Scraping

Headquarters

San Francisco, CA

Sample tests· showing 3 of 9

#InputExpected behaviorCheck
01

Agent only needs the list of URLs on a site to plan work, but starts a full crawl that scrapes every page's content.

Use POST /v1/map to enumerate URLs quickly without scraping each page's content. Reserve /v1/crawl for when page bodies are actually needed. Map is the cheap, fast discovery primitive; crawl is the expensive content primitive.

Pass / FailAi Platformmedium
02

Agent wants only documentation URLs from a large site and pulls the full map, then filters thousands of links client-side.

Pass the map search parameter (e.g. search='docs') to bias discovery toward matching URLs at the source, reducing the result set and downstream filtering. Treat search as a relevance filter over discovered URLs, not a web search.

Pass / FailAi Platformmedium
03

A map call on a very large site returns far more URLs than the agent can process; it has no limit set.

Set a sensible limit on the map request to cap the returned URL count to what the pipeline can handle, then paginate or refine with search if more are needed. Avoid pulling an unbounded URL list into a fixed-size downstream stage.

Pass / FailAi Platformmedium

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How this eval is graded

Grade against expected.ideal_behavior and expected.rubric. Per-criterion pass requires mean >= 4.0 and no criterion below 3.

Rubric criteria

  • Firecrawl
  • Ai Platform
  • Map Url Discovery

Recommended for

FirecrawlFirecrawl customers

Works with

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Frequently asked questions

What does the Map Url Discovery eval for Firecrawl Firecrawl test?+

Evaluates Firecrawl's Map (URL discovery) across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Web Data for AI eval coverage.

How is the Map Url Discovery eval scored?+

The judge rubric: Grade against expected.ideal_behavior and expected.rubric. Per-criterion pass requires mean >= 4.0 and no criterion below 3.

How many test cases does this eval pack include?+

The Map Url Discovery pack for Firecrawl Firecrawl contains 9 test cases. 3 sample cases are shown free on this page; the full set runs in a Corsac workspace.

How do I run this eval?+

Sign up for Corsac, connect your model or agent endpoint, and run the Map Url Discovery pack as-is or after customizing thresholds. Results land in your workspace with per-case scores, and you can gate releases on the pack in CI via the REST API.

Run this eval in your workspace

Connect your data, configure thresholds, and review results with your team.