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page_model

Renders web pages as structured JSON objects, including forms, links, and cards, scored by relevance to your task.

Instructions

Render the current page into semantic, task-discoverable JSON objects. Reconstructs page structure as search_form, nav_link, article_card, course_card, model_card, product_card, table, answer_block, and limitation objects with actions, normalized fields, goal-based scoring, and provenance. Prefer this as the first planning tool after navigate when raw links/text are too wide.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
goalNoOptional task goal/query used to rank objects by relevance.
limitNoMax objects to return (default 50).
typesNoOptional object kinds to return, e.g. search_form, article_card, model_card, course_card, card, table, answer_block.
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 explains that the tool reconstructs page structure into semantic objects with scoring and provenance, but does not mention side effects, permissions, or error cases. The description provides sufficient context for a read-like tool but lacks depth on behavioral traits.

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?

Two sentences that efficiently convey purpose, output details, usage recommendation, and key features. No unnecessary words; every sentence earns its place.

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 tool's functionality and usage context well, listing object types and features. It lacks details on return format (no output schema) and error handling, but is sufficiently complete for an extraction tool with good sibling differentiation.

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 coverage is 100%, so the schema already describes all three parameters. The description adds minimal value beyond the schema, mentioning 'goal-based scoring' which relates to the goal parameter but not specifying format or constraints.

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 clearly states the tool's verb ('Render') and resource ('current page'), and lists specific output object types. It also distinguishes itself from siblings by recommending use when raw links/text are too wide, positioning it as a first planning tool after navigate.

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?

Explicitly advises to prefer this tool as the first planning step after navigate when raw links/text are too wide. While it does not list explicit exclusions or alternatives, the guidance is clear and actionable.

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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