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Browserless Smart Scrape

browserless_smart_scrape
Read-onlyIdempotent

AI-assisted extraction: tries a plain HTTP fetch first and falls back to a full headless browser, returning content in the requested formats (html, markdown, links, screenshot, pdf). Best when you just want clean page content and do not know the right selectors. Example: browserless_smart_scrape({ url: "https://example.com", formats: ["markdown", "links"], _apiKey: "your-token" })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesThe page URL to extract
_hostNoOptional region host override
proxyNoProxy pool: "residential" (default) or "datacenter"
_apiKeyYesBrowserless API token (sign up at https://www.browserless.io/signup)
formatsNoWhich formats to return: any of "html", "markdown", "links", "screenshot", "pdf" (default ["html"]). "content" (raw HTML/parsed JSON) is always returned.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint, idempotentHint, destructiveHint), the description discloses the fallback mechanism from HTTP fetch to headless browser, the configurable output formats, and the fact that raw 'content' is always returned. These are meaningful behavioral details an agent cannot infer from annotations alone.

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 three sentences with no filler: behavior, usage guidance, and a concrete example. Core information is front-loaded, and every sentence contributes to the agent's understanding of what the tool does and how to call it.

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?

The description covers the main behavior, fallback strategy, formats, and an example call. It does not explain error handling, timeouts, or the exact structure of returned content, but the schema and annotations fill most gaps. For a tool with no output schema, this is reasonably complete.

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?

Schema description coverage is 100%, so the schema already documents all parameters. The description adds a concrete usage example and lists the format options, but it does not add deeper semantic meaning for parameters like _host or proxy beyond what the schema provides. This matches the baseline for fully documented schemas.

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 states a specific action ('AI-assisted extraction') and resource ('page content'), and clearly explains the two-step behavior (HTTP fetch then headless browser fallback). It also distinguishes itself from sibling tools by noting it is best when the user does not know the right selectors, which separates it from browserless_scrape and browserless_content.

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?

The description explicitly says when to use this tool: 'Best when you just want clean page content and do not know the right selectors.' This gives clear context for selection, though it does not explicitly name alternatives or state when not to use it. The guidance is useful but not exhaustive.

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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