extract_products

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

With no annotations, the description carries the burden. It discloses the method (reads JSON-LD/OpenGraph/microdata) and that it stores rows via save_as, which implies a side effect of saving data. However, it does not mention potential issues like rate limits, errors, or the effect of js_render, leaving gaps in behavioral disclosure.

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 two sentences, front-loaded with the core purpose, and includes no fluff. Every clause adds value—specifying fields, method, compatibility, and save_as usage. It is concise and well-structured.

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 tool's moderate complexity and presence of an output schema, the description covers the main behavior, method, and parameter for saving. However, it leaves out js_render entirely and does not mention any limits or edge cases, making it slightly incomplete. The output schema reduces the need to describe return values, but the missing parameter is a notable gap.

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?

The description explains urls (list of product URLs) and save_as (stores rows for querying) but completely omits js_render. With schema coverage at 0%, this is insufficient. It adds some meaning beyond the schema but does not fully compensate for the unmentioned parameter, so a score of 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 description clearly specifies the action (extract structured product data) and the resource (list of product URLs), listing specific fields like name, price, currency, etc. It also distinguishes itself from siblings like extract_by_selector by explicitly noting it uses JSON-LD/OpenGraph/microdata instead of selector guessing, making the purpose unambiguous.

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?

It states the tool 'works across most storefronts without configuration' and implies it is for structured product data, which helps decide when to use it. However, it does not explicitly state when not to use it or mention alternatives like extract_by_selector, though it contrasts with selector-based guessing. This provides context but lacks explicit exclusions.

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