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Crawlora-org

Crawlora MCP

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web_scrape

Fetch any public URL and convert it to clean markdown, HTML, links, or metadata. Uses fast HTTP first, then escalates to a real browser when the page is blocked or JavaScript-rendered, returning only the main content.

Instructions

Scrape a URL into markdown, HTML, links or metadata. Fetches a single public URL and returns clean content in the requested formats (markdown, html, raw_html, links, metadata). The request body IS the ScrapeOption object itself — e.g. {"url": "https://example.com"} — do not wrap it in an extra key. With render=auto the request starts as a fast HTTP fetch and escalates to a real browser when the page is blocked or rendered with JavaScript; backend only pins a specific headless-browser engine for the browser tier and is not a render mode. only_main_content (default true) strips navigation, headers, footers and other boilerplate before conversion. Only public pages are supported; respect each site's terms of use and robots directives. A handful of popular sites (Amazon, Reddit, Yelp, LinkedIn, and others) already have a dedicated, more reliable endpoint elsewhere in this API — a failed scrape against one of them names it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scrapeOptionYesScrape options

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Addedv1.1.0

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full behavioral disclosure burden, and it does well: it explains render=auto escalation, the browser-engine nuance, only_main_content default behavior, and supported formats. However, the instruction that the body IS the ScrapeOption object conflicts with the schema's required scrapeOption wrapper, and error/rate-limit behavior is not addressed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but every sentence carries distinct operational information, with the main purpose front-loaded. There is no filler or redundant restating of the schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no annotations and no output schema, it covers most of what an agent needs: formats, request shape, render behavior, content stripping defaults, public-page constraints, and dedicated alternatives. The one serious gap is the mismatch between the description's body-shape instruction and the schema's required wrapper.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds genuinely useful parameter meaning beyond the generic scrapeOption property, including the request-body example, render=auto behavior, and the only_main_content default. But it tells the agent not to wrap the body in an extra key while the schema requires a scrapeOption wrapper, creating real ambiguity about the correct argument shape.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'Scrape a URL into markdown, HTML, links or metadata' and clarifies it fetches a single public URL. It also distinguishes itself from the many site-specific sibling tools by noting that popular sites have dedicated, more reliable endpoints elsewhere.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives clear context for when to use the tool: only public pages are supported, and popular sites like Amazon, Reddit, Yelp, and LinkedIn are better served by dedicated endpoints. It does not enumerate all alternatives, but the conditional fallback guidance and robots/terms note provide solid selection guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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