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extract_cards

Detect and extract repeated card-like blocks (articles, products, courses) from a webpage, returning normalized data including title, price, and availability. Use when selectors are unknown.

Instructions

Auto-detect repeated article/card/product/course/listing blocks and return normalized items [{title, price, condition, url, availability, snippet, meta, image_alt, score}]. Prefer this over extract_list when the page has semantically ambiguous recipe, course, product, or model cards and you do not already know field selectors. Optional selector scopes detection to known card nodes; kind can bias scoring (recipe, course, product, listing).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNoOptional hint: recipe, course, product, listing, article
limitNoMax items to extract (default 50)
selectorNoOptional CSS selector matching each card/listing block
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses auto-detection behavior, normalization, and optional parameter effects. However, it does not mention side effects (e.g., whether it modifies page state), performance characteristics, or error handling. Adequate but not comprehensive.

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, no waste. First sentence states action and output structure; second provides usage guidance and parameter hints. Front-loaded with core purpose. Every word earns its place.

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

Completeness5/5

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

Given no output schema, the description lists returned fields. Sibling tools (especially extract_list) are addressed. Parameters are explained both in schema and description. The tool's behavior is sufficiently characterized for an auto-detection tool with zero required parameters and 3 optional ones.

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 description coverage is 100% (all 3 parameters described). The description adds context beyond schema: explains the 'kind' enum values (recipe, course, product, listing), that 'selector' scopes detection, and 'limit' default 50. This adds meaningful value.

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 auto-detects repeated blocks (article/card/product/course/listing) and returns normalized items with a specific schema. It also distinguishes from sibling 'extract_list' by explicitly noting when to prefer this tool.

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?

Provides explicit guidance: 'Prefer this over extract_list when the page has semantically ambiguous...cards and you do not already know field selectors.' Also explains optional selector and kind biasing. Missing explicit when-not-to-use scenarios, but is otherwise clear.

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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