Avahit

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

Annotations already cover the safety profile (readOnlyHint=true, openWorldHint=false), and the description adds genuine behavioral context by disclosing the ranking factors behind the match percentage: same category, price distance, product type, same store. It does not describe result ordering beyond 'ranked' or any result-count limits.

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?

A single front-loaded sentence that delivers the input, the output, and the scoring factors with no filler. The trailing parenthetical is dense but each element earns its place.

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 one-parameter, read-only lookup with no output schema, the description explains both the return shape (ranked alternatives with a match percentage) and the criteria behind it. That is close to complete, with only id format and result limits unaddressed.

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?

There is a single parameter and schema description coverage is 0%, so the schema contributes only its type and required status. The description adds a useful qualifier ('Avahit product id'), but does not specify the format or where the id comes from, so it only partially compensates.

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 verb (return ranked alternatives) plus the resource (alternatives for a given Avahit product id) and even names the ranking signal. It is clearly distinct from siblings like search_products and get_price_history, though it never explicitly contrasts itself with them.

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?

There is no statement of when to use this tool versus search_products or the other siblings, and no prerequisites or exclusions are given. Usage is only inferable from the name and the phrase 'Given an Avahit product id.'

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 establish readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds genuine behavioral context beyond that: prices are Avahit's own observations, freshness is tracked via last-verified timestamps, and Amazon prices carry a hard 24-hour retention limit due to licensing. It does not say whether Amazon ids error out or return empty.

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?

Two sentences, front-loaded with the core action and the identifier requirement before enumerating the returned fields. The field enumeration is dense but each item is distinct information an agent could use; the Amazon caveat is correctly placed last as an exception note.

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 usefully enumerates the returned fields and explains the one major data gap (Amazon history), which is exactly the kind of thing an agent needs. Remaining omissions — error behavior for unknown or Amazon ids, and whether results are paginated — are minor for a single-parameter read 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 description coverage is 0% for the single productId parameter, so the description must carry the load. It does add meaning by specifying the id is an 'Avahit product id' (distinguishing it from an Amazon identifier), but it gives no format, length, or validation guidance beyond that.

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 gives a specific verb (return) and a precisely scoped resource: what Avahit itself observed about a product's price, enumerated as current price, verification date, low/high with dates, average, and check count. It implicitly separates itself from get_store_sales by emphasizing Avahit-observed rather than store-reported data, but it never names or contrasts a sibling explicitly.

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?

Usage is implied by the phrase 'Given an Avahit product id' plus the Amazon caveat, which tells the agent this tool is useless for Amazon products. However, there is no explicit when-to-use statement and no routing to alternatives such as get_store_sales or compare_alternatives.

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 establish readOnlyHint=true and openWorldHint=false, and the description adds genuinely useful disclosure beyond that: the data covers both homepage announcements and email campaigns, distinguishes measured price cuts from checkout-only discounts, and explicitly states 'Promo codes are never included' — a valuable negative guarantee. It does not discuss rate limits or freshness/latency, keeping 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?

It is a single long sentence that is front-loaded with the governing question, but the tail becomes a run-on list ('price cuts Avahit measured itself, today's homepage offer, holiday sale pages...') that is harder to parse than a short bulleted enumeration would be.

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 return content, and it does so thoroughly — dates, price-drop verification, homepage offers, holiday pages, restock cadence. Missing only structural details such as response format or whether results span a default time window.

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% for the single 'store' parameter, so the baseline is 3. The description repeats the same examples as the schema ('glossier.com', brand names) without adding format, normalization, or lookup-behavior details, so it does not exceed 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?

The description names a specific resource (a brand store's announced sales) and enumerates what is returned: announcement dates, whether prices actually dropped, homepage offers, holiday sale pages, and restock frequency. It is clearly distinct from siblings like get_price_history or search_products, though it never explicitly contrasts itself with them.

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 framing question 'When does a brand store have sales?' implies the use case, but there is no explicit when-to-use, no when-not-to-use, and no mention of the three sibling tools or how to choose between get_store_sales and get_price_history/compare_alternatives.

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 only declare readOnlyHint and openWorldHint, so the description adds real value beyond them: it discloses data freshness ('re-checks at the store every day') and enumerates the returned fields, including the last-check date. It does not mention rate limits, result ordering, or empty-result behavior, keeping 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?

Two tight sentences: the first establishes scope and data freshness, the second lists return values. Nothing is repeated from the name or schema, and the most decision-relevant fact (daily price re-checks) is front-loaded.

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 listing the returned fields and their meaning (price with last-check date, store domain, rating, product link). Combined with a fully documented input schema and read-only annotations, an agent has what it needs; only result ordering and empty-state behavior are unaddressed.

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%, with both `query` (example provided) and `limit` (range and default) documented in the schema itself. The description adds no syntax, format, or matching-behavior detail for the query parameter, so the 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 gives a specific verb+resource ('Search Avahit's catalog of direct-to-consumer brand products') and adds a meaningful scope qualifier: prices are re-checked at the store daily. It never names the siblings (compare_alternatives, get_price_history, get_store_sales), so an agent gets a clear purpose but no explicit routing signal.

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?

Usage is only implied – an agent can infer this is the entry point for finding products, but there is no statement of when to use it versus get_price_history or compare_alternatives, nor any prerequisites or exclusions. Minimum-viable guidance rather than explicit direction.

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