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

With no annotations, the description carries the full burden and does it well. It discloses that the tool scrapes candidate pages, verifies images via HTTP, deduplicates near-identical crops, maintains a TTL disk cache, and returns a status field. It does not cover rate limits or failure modes, but the core behavior is clearly described.

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

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

The description is well-structured with bold section headers: summary, usage conditions, example queries, and return fields. It is appropriately sized and front-loaded; every section adds meaningful guidance without verbose filler.

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

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

For a web-extraction tool with four parameters and an output schema, the description covers the workflow, return format, and example invocations. It could better disambiguate from the sibling tool get_product_images_tool, but the provided detail is sufficient for an agent to call it correctly.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds value with concrete example queries showing how to pass product names, URLs, max_images, and site_filter, which reinforces the schema's documentation without being redundant.

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

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

Description clearly states a specific verb and resource: 'Universally extracts verified, high-resolution product photography and official sources across the web.' It also conveys a distinct approach (automatic discovery, HTTP verification, deduplication) that differentiates it from generic scrapers, though it doesn't explicitly name or contrast sibling tools.

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

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

The 'USE THIS TOOL WHEN' section explicitly lists the scenarios for using this tool: needing product photos, packshots, visual references, or master CDN assets. It also describes the automatic discovery and verification behavior. It does not state exclusions or alternatives, but the context is clear enough for an agent to select it.

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

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

No annotations are present, so the description bears the full burden of behavioral disclosure — and it says nothing about behavior. An alias claim implies identical behavior to fetch_product_images, but the description never states what that behavior is: no filtering semantics, no read-only guarantee, no output format, no side effects.

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

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

The text is a single, well-front-loaded sentence with no waste. However, its brevity is achieved at the expense of substantive content — it conveys a single fact (alias target) and nothing else, so conciseness is not paired with information density.

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

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

Despite having an output schema (which excuses return-value explanation), the tool still needs to explain its behavior and parameters. With 3 undocumented parameters, no annotations, and zero behavioral description, an agent cannot reliably decide to call it or construct valid arguments. The alias reference defers all meaningful context to an unseen sibling.

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

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

Schema description coverage is 0% and the description mentions none of the three parameters (query, max_images, site_filter). The schema itself contains no descriptions and no enums, so the agent is left with bare type information and defaults, with zero guidance on semantics — including what query expects or how site_filter behaves.

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

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

The description states only that it is an alias for fetch_product_images, never that it retrieves product images. The actual function must be inferred from the tool name and the sibling's identity. This is a meta-description referencing another tool rather than a description of the tool's own purpose — close to a tautology of the name.

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

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

The description identifies fetch_product_images as the canonical target but gives no guidance on when to call this alias versus the canonical tool, nor any information on how it relates to search_product_urls or scrape_single_url. No context is given on preferred use cases.

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

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

With no annotations provided, the description carries the full burden. It adds context by stating 'Extraction only' (read-only behavior) and listing the technical methods (JSON-LD, OpenGraph, dynamic JS datasets, DOM selectors, CDN upscalers), which implies JavaScript execution and network requests. It does not mention rate limits or auth, but for a scraping tool with a clear read-only hint, this is meaningful.

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

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

The description is well-structured and front-loaded with the core purpose, followed by brevity in 'when to use', a concrete example, and expected return. Every section earns its place, with no filler or redundancy.

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

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

Given the output schema exists, return values are covered. The description provides usage context, source of URLs, and technical methods. It lacks error handling details or edge-case behavior, but for a simple scraping tool, it is reasonably complete.

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

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

Schema description coverage is 100%, so the baseline is 3. The description does not add meaning beyond the schema; the example call illustrates a valid URL but does not clarify parameter semantics further. The schema already documents each parameter adequately.

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

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

The description clearly states the specific verb 'Extracts' and the resource 'high-resolution product imagery from a single direct product URL', which distinguishes it from sibling tools like search_product_urls that search for URLs. The 'Extraction only' parenthetical further clarifies its scope.

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

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

The 'USE THIS TOOL WHEN' section explicitly states the condition: when the user already has an exact product URL. It references search_product_urls as a source, providing context, but does not explicitly name alternatives to use instead when the condition is not met. It is clear but lacks explicit when-not guidance.

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

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

With no annotations, the description carries the full burden of disclosing behavior. It clearly states 'Discovery only,' explains that it does not download or scrape images, and notes that it returns 'ranked' URLs — this goes beyond the mere title and gives the agent expectations about side effects and output ordering. It doesn't cover rate limits or auth, but these are less critical for a discovery-only read operation.

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

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

The description is well-structured with clear headings and short bullet sections. It front-loads the core purpose in one sentence, and every section (why, examples, returns) earns its place without redundant wording or filler.

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

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

For a simple three-parameter discovery tool with complete schema documentation and an output schema, the description is sufficient for an agent to select and invoke it correctly. It gives purpose, usage context, concrete examples, and a clear statement of the return type, so no critical information is missing.

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

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 each parameter's meaning and example values. The description adds example calls showing how query, max_urls, and site_filter are combined, but this does not materially extend the parameter semantics beyond what the schema provides. Baseline 3 is appropriate.

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

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

The first sentence states a specific action and target: 'Discovers and ranks candidate e-commerce product pages for a given query (Discovery only).' It clearly identifies this as a discovery/preview tool rather than a scraping tool, and distinguishes it from siblings like fetch_product_images and scrape_single_url by explicitly noting it does not download or scrape images.

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

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

The 'USE THIS TOOL WHEN' section provides clear use cases: finding authoritative PDPs and previewing URLs before scraping. This effectively implies when not to use it (when you need images or actual page content), though it never names sibling alternatives directly or states explicit exclusions. The example calls further clarify how to invoke it.

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