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browser_snapshot

Capture a compact, token-efficient page snapshot with numbered interactive elements, labels, headings, and iframe details. Scope by CSS selector or viewport to reduce tokens.

Instructions

Capture a compact, token-efficient snapshot of the active page showing interactive elements with numbered references (@1, @2, ...), smart labels (icons/images/CSS backgrounds), headings, and accessible iframes. Supports scoping to a CSS selector and viewport-only filtering.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
selectorNoOptional CSS selector to scope snapshot to a specific DOM container
in_viewportNoIf true, only include elements currently visible in viewport
max_elementsNoMaximum interactive elements to include (truncates remainder to save tokens)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.8.0

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses useful behavioral traits (token efficiency, numbered reference scheme, smart labeling, iframe access) but omits whether this is a pure read, whether it triggers any page interaction, and any permission or state requirements. It adds value beyond a tautological restatement but leaves meaningful gaps.

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

Conciseness4/5

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

Two sentences that are front-loaded with the core action and output shape, with the scoping capabilities in a compact trailing clause. Efficient with no filler, though slightly dense in its enumeration of output types.

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?

No output schema exists, and the description does the work of explaining what the snapshot returns (numbered refs, labels, headings, iframes), which is the right division of labor. All three parameters are covered by the schema and two are echoed in prose. The main gap is the absence of any usage steering among the many browser siblings.

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 three parameters, making 3 the baseline. The description reinforces the selector-scoping and viewport-filtering behavior but adds no syntax, format, or edge-case detail beyond the schema, and it omits any mention of max_elements.

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

Purpose4/5

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

States a specific verb and resource (capture a snapshot of the active page) and details the substance of the output: numbered references, smart labels, headings, iframes. This clearly distinguishes it from visual siblings like browser_screenshot and raw-markup siblings like browser_get_html, though it never names those alternatives directly.

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

Usage Guidelines2/5

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

The description explains what the snapshot contains but gives no when-to-use guidance, no conditions for preferring it over browser_screenshot, browser_get_html, or browser_find_in_page, and no exclusions. The 'token-efficient' phrasing hints at LLM-context use but stops short of explicit routing advice.

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