reusefulshop

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

With no annotations provided, the description does useful work by stating that the tool is read-only and free. It also discloses the returned verdict categories, though it does not explain data recency, permissions beyond read-only, or how the market range is tracked.

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 tight sentences plus one example. The usage condition is front-loaded, followed by behavior and output, with no wasted wording.

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 two-parameter read-only tool with no output schema, the description covers when to use it, what it compares, and the possible verdict values. Nothing essential for correct invocation 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 coverage is 100%, so the schema already defines both parameters. The description adds value with a concrete example showing that q is a detailed product query string and price is the asking price, clarifying intended input format beyond the terse schema labels.

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 and resource: compare an asking price against a tracked market range and return a deal verdict. It distinguishes this from sibling tools by emphasizing the user-supplied asking price and the verdict output.

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 explicitly says to use this when the user has a specific asking price to judge, which is clear context. However, it does not name or contrast alternatives like get_worth, so an agent must infer when a different pricing tool is better.

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 and does add real behavioral context: read-only, free, and the caveat that history only starts when tracking began and may be short. It stops short of describing pagination or the exact response shape, but the safety and data-availability profile is well covered.

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, front-loaded with the primary use case, then the caveat, then the sibling routing. Every clause 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?

There is no output schema, so the description correctly states what is returned (daily price snapshots) and its limitation (history may be short). Combined with the read-only/free note and sibling routing, nothing essential is missing for correct invocation.

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% and there is only one parameter, so the schema already defines 'q'. The description reinforces the resource ('an item') but adds no syntax, format, or identification guidance beyond the schema, 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?

States a specific verb and resource: returns tracked daily price snapshots for an item, framed as a price-trend tool. It also explicitly distinguishes itself from the sibling get_worth, so an agent can route without opening either 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?

Gives the when-to-use condition ('price trends over time') and names the alternative with its selecting condition ('For a single current value prefer get_worth'). This is explicit routing guidance, not 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?

No annotations are provided, so the description carries the burden and does disclose that the call is read-only, free, and sourced only from the operator's own shop (a meaningful data-coverage caveat). It does not mention rate limits, failure modes, or how sparse the sample may be when the shop has no matching sales.

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 short sentences, zero filler, front-loaded with the use case and the return payload before the sibling comparison. Every clause carries selection-relevant information.

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 summarizes the return shape (median/low/high sold prices) and the data source. Only minor gaps remain: no note on result sparsity or what happens when the item has no sold history in the operator's shop.

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?

Only one parameter exists and its schema description coverage is 100%, so the schema already defines 'q'. The description implies the query is an item but adds no syntax, matching, or formatting detail beyond that, which is baseline-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?

States a specific resource ('real completed transactions') and a concrete return payload ('median/low/high sold prices'), scoped to the operator's own eBay shop. It also explicitly separates itself from sibling get_worth, so an agent can pick it apart from alternatives without reading the 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?

Gives an explicit selection rule: prefer this for actual sold comparables, use get_worth for live asking-price estimates. That is a when/when-not pair anchored to a named sibling rather than implied context.

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 and does disclose useful traits: read-only, free for cached items (implying non-cached may incur cost), and the marketplace data sources and currency. It stops short of stating latency, rate limits, or what happens for uncached items, leaving a gap for a mutation-free but potentially costly lookup.

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?

One dense sentence plus a short example; the purpose and return shape are front-loaded with no filler. Minor run-on structure but every clause (sources, currency, cost, example) 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 single-param lookup with no output schema, the description covers purpose, returned fields, data provenance, currency, and cost model. It omits edge-case behavior (no data found, uncached pricing), which is the only meaningful remaining 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?

Schema coverage is 100% with a single, well-described 'q' parameter, so the baseline is 3. The description adds a concrete good-query example ('used playstation 5 console'), which demonstrates the expected natural-language item format beyond the terse schema hint.

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+resource ('current second-hand value check') and enumerates the return shape (low/typical/high price plus confidence) and data sources (eBay/Discogs in GBP). This is clear and distinct from get_history/get_sold, though it never names those siblings explicitly to sharpen the contrast.

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 for a current second-hand value check' gives an explicit trigger, and 'current' implicitly frames it against the historical siblings. However, no alternatives or when-not-to-use conditions are stated, so the agent still infers routing against check_deal/get_history/get_sold.

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