Strong Saga Home Gym Gear Lookup

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

Annotations only say readOnlyHint=true and openWorldHint=false; the description adds substantial behavior beyond that: selection is constrained to products whose stored guide data fits the limits, skipped items are reported with reasons, budget_needed indicates the entry price for a full set, and buy_url must be shown verbatim. It also discloses the strength sub-branching (barbell vs dumbbell setup) that determines what actually comes back, which an agent could not infer from annotations or schema.

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?

Front-loaded with the usage trigger and examples before the return-shape details, so an agent gets the decision-relevant information first. The middle return-format sentence is dense and multi-clause, but nearly every clause carries distinct information (item fields, total, skipped, budget_needed) rather than repetition.

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?

With no output schema, the description carries the burden of describing returns and does so field by field (why_it_fits, guide_url, buy_url, total, skipped, budget_needed). What it does not cover is edge behavior: what happens if nothing fits the constraints at all, and whether budget_needed is always present, leaving some ambiguity for a fairly complex tool.

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 80%, so baseline is 3; the description earns above that by giving real semantics for the undescribed `goal` enum (strength → barbell or dumbbell setup, cardio → one machine, both → strength plus cardio) and by tying floor/ceiling constraints to the selection filter. Budget and unit semantics are already in the schema, so it adds no new detail there.

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?

States a specific verb-and-resource pair (build a starter home gym set within budget and space) and immediately grounds it in two concrete user phrasings. The description also explains the composition logic per goal, which distinguishes it from the sibling gear lookup/compare/search tools that return individual products.

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?

"Use when a user asks what to buy for a home gym within a budget and space" plus two example queries gives a clear trigger condition and implicitly the trigger is a bundle recommendation rather than a single-item lookup. It never names the sibling tools or states when NOT to use it (e.g. for a single product lookup, use get_home_gym_gear), so the routing to alternatives is left to inference.

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?

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds real beyond-schema behavior: results must be built only from guide data, guide_url and buy_url must be carried per product, and buy_url must be shown verbatim – useful output constraints an agent would otherwise not know.

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?

Three sentences, front-loaded with the trigger condition, then outputs, then output constraints. Every sentence carries information, though the fit-note list (ceiling, floor space, capacity, price) is slightly enumerative.

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?

With no output schema, the description carries the burden of explaining returns and does so (specs table, fit notes, plain summary, URLs preserved). A read-only, 2-param tool is well covered; only the height parameter's effect on fit notes is left to the schema.

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 both ids and user_height_in. The description reinforces the id source and example format but does not add syntax, defaults, or clarify that user_height_in drives the ceiling math beyond what the schema says. Baseline 3 applies.

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 a specific action (put 2 or 3 products side by side), the artifacts it produces (specs table, fit notes, plain summary), and the source it draws from (guide data). It also names the sibling search_home_gym_gear as where the ids come from, so an agent can distinguish it from search/get/build tools without opening a schema.

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?

Explicit trigger phrasing ('Use when a user asks "A or B?", "what's the difference between these"...') makes the selection condition unmistakable. It doesn't state when NOT to use it or contrast with get_home_gym_gear for single-product lookups, so it falls short of a full 5.

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?

Annotations already declare readOnlyHint=true, so the description is free to focus on behavior: it lists the returned fields and adds a non-obvious output-handling rule ('Show buy_url to users exactly as given'), which the agent could not derive from structured data. It does not state what happens with an invalid or unknown id, which keeps it short of a 5.

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?

Front-loaded with the usage trigger, followed by the return payload and the buy_url instruction; every sentence carries information. There is minor redundancy between 'one product you already have an id for' and 'Full record for one product id'.

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?

With no output schema, the description does the work of enumerating returned fields, which is exactly what an agent needs before calling. The remaining gap is failure behavior for a non-existent id and confirmation that all fields are always present.

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 both parameters are already documented, and the description mostly echoes that (id example sg-001, and the ceiling-math relevance implied by 'ceiling needed'). Baseline 3 is appropriate; the description adds context but no format or edge-case guidance beyond the schema.

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?

States a specific verb and resource: retrieve the full record for a single product id, and enumerates what that record contains (size, ceiling needed, capacity, price, retailer, buy_url). It is clearly distinguishable from search/compare/build siblings because it operates on an id the caller already holds.

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?

Opens with an explicit trigger condition ('Use when a user asks about one product you already have an id for') plus concrete example questions, and names the siblings (search, compare, build) as the source of that id. The only mild inference required is that a missing id means go to search_home_gym_gear, which the id parameter description reinforces.

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?

Goes well past the read-only annotation: it discloses the return payload fields, warns that buy_url is an affiliate redirect that must be surfaced verbatim, and describes failure behavior (errors naming the parameter, valid values and an example) and silent-drop behavior (ignored parameters listed in warnings). These are operational facts an agent could not infer from the schema.

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?

Front-loaded with the trigger condition and examples before any enumeration, and every later sentence (returns, buy_url handling, error/warning semantics, q fallback) carries distinct information. The long parenthetical product list partly duplicates the category enum already in the schema, which is the only real padding.

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?

With no output schema, the description compensates by naming the returned fields, and with 9 optional parameters it still makes clear what the tool matches on and what happens on bad or unused input. An agent has enough to call this correctly on the first attempt without additional discovery.

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 bar is baseline 3, but the description contributes a cross-parameter decision rule the schema lacks: use q when the category name is uncertain. The natural-language examples ("power rack under $500 for a 7 ft ceiling", "walking pad for a 6 ft 2 in person") also show how user phrasing maps onto max_price, max_ceiling_in and user_height_in combinations.

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?

States a specific verb and resource plus the exact constraint axes (budget, ceiling, capacity, floor space) and enumerates the product space, so an agent immediately knows this is an attribute-filtered catalog search. It does not explicitly contrast with get_home_gym_gear, compare_home_gym_gear or build_home_gym, so the sibling boundary is inferred rather than stated.

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?

"Use when a user asks for a specific kind of home gym gear that fits their space, budget or body" is a real trigger condition, reinforced by four concrete query examples. It also supplies a fallback decision rule ("If unsure of the category name, pass keywords in q"), but never says when NOT to use it or which sibling to pick for a lookup/compare/build task.

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