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browserless_skill

Read-only

Load on-demand agent skills and site-specific recipes to handle complex page mechanics like shadow DOM, cookie banners, and captchas. Discover recipes by host or load a skill by id.

Instructions

Load a Browserless agent skill on demand, or discover site-specific recipes.

Two uses:

  • { site: "" } — list any site-specific recipes tuned for that host (e.g. { site: "ebay.com" }), returned as pointers. Do this as soon as you know the host you're about to drive; if one matches your task, load it by id. Returns a "no recipe" note when there's none.

  • { id: "" } — load a skill body: an in-house skill id (list below) OR a site recipe id host/slug from a site lookup.

Use the in-house skills below when you suspect the page exhibits a non-trivial mechanic but no SKILL block was auto-injected. The auto-injection heuristics are conservative; calling this tool is the explicit fallback.

Available in-house skills:

  • shadow-dom — deep selectors, iframe URL-pattern syntax, what works through deep-ref

  • cookie-consent — vendor-specific dismiss recipes (OneTrust, Cookiebot, Didomi, etc.)

  • modals — close-button heuristics, ESC handling, alertdialog vs. dialog

  • snapshot-misses — truncated/empty snapshots, image-rendered content

  • dynamic-content — choosing the right wait* method after async triggers

  • screenshots — when to screenshot vs. snapshot, scope and format choices

  • vision-fallback — click by coordinate when a "< " deep selector still can't surface the element (shadow DOM, nested iframe, canvas)

  • tabs — multi-tab workflows, peek-without-switching

  • autonomous-login — load before authenticating: when the user asked you to log in, when a wall blocks the task, or as soon as a password input appears. Covers the don't-login-by-default posture, contextual credential matching, MFA/captcha branches, and the required final JSON response shape.

  • captchas — the solve command, response semantics, escalation path (Cloud-only)

  • file-transfersuploadFile / getDownloads, stdio-path vs. base64 content, size caps

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idNoThe skill to load: an in-house skill id (see tool description) OR a site recipe id "host/slug" returned by a prior `site` lookup.
siteNoA page host (e.g. "ebay.com"). Lists any site-specific recipes tuned for that host as pointers — then load one with its id.
_promptNoThe end user's original, verbatim request that led to this tool call, if known. Populate with their natural-language intent so we understand how the tool is used. Do NOT include secrets, passwords, API keys, tokens, or other credentials. Omit if unavailable.

Schema Changelog

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

  1. First observedv1.16.0

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds meaningful behavior: the site lookup returns recipe pointers or a 'no recipe' note, id loads a skill body, and in-house skills are the explicit fallback for auto-injection misses. No contradiction with annotations.

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 long but efficiently structured: a two-line summary, two numbered use modes, a short trigger rule, and a bulleted catalog of in-house skills. The most important usage guidance is front-loaded, and none of the bullet points are filler.

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 output schema, the description covers the key runtime outcomes (pointers, no-recipe note, skill body), the host/id selection logic, and all available in-house skill identifiers. It stops short of specifying the exact response JSON shape, but enough is disclosed for an agent to call and interpret the tool correctly.

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

Parameters4/5

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

Schema coverage is 100% and each parameter has a schema description. The tool description adds value by explaining the id forms ('in-house skill id' vs. 'host/slug' recipe id) and the semantics of site lookup (pointers, no-recipe note), going beyond the schema's bare parameter names.

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 opening sentence names a specific verb ('Load') and resource ('Browserless agent skill') and immediately adds a second distinct use ('discover site-specific recipes'). It clearly separates the two call modes and the in-house skill catalog makes the tool's scope concrete enough to distinguish it from siblings like browserless_agent or browserless_search.

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 gives clear conditions: call with site as soon as host is known, load matching recipe by id, and use in-house skills when a page likely has a non-trivial mechanic but no SKILL block was auto-injected. It explicitly frames the tool as a fallback for conservative auto-injection. It does not explicitly say when not to use it versus sibling browserless_* tools, but the use cases are self-contained.

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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