oracle

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

No annotations are provided, so the description carries the full burden. It discloses that it checks 'recent' drops/restocks and returns 'in-stock matches with direct buy links.' However, it does not specify the time window for 'recent,' which shops are monitored, or any limitations (e.g., regional availability). This leaves some behavioral ambiguity.

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: the first states the action and result, the second provides the use case. It is front-loaded with key information, and every word adds value. Zero waste.

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?

The tool is simple with 2 parameters, both fully described. The return type is indicated ('recent in-stock matches with direct buy links'). No output schema exists, but the description sufficiently covers what the agent can expect. No further details are necessary 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 coverage is 100% with descriptions for both niche and query. The description adds practical examples ('Prismatic Evolutions', 'Millennium Falcon') that clarify the query format beyond the schema's generic 'Product name to match against recent drops.' This enhances usability.

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 tool checks for recent drops or restocks of a hobby item and returns matches with buy links. It distinguishes itself from sibling tools like check_recall (recalls) and find_settlements (settlements) by focusing on in-stock status for collectibles.

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 explicitly says 'Use when an agent wants to find where a sold-out collectible is currently available,' providing a clear usage context. However, it does not explicitly state when not to use it (e.g., for general price checks, which is covered by check_scalper_price), though the sibling list implies differentiation.

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 provided, so description carries full burden. It discloses that recalls come from agencies like CPSC, FDA, NHTSA and returns specific fields. However, it lacks details on limitations, pagination, or performance characteristics, making it adequate but not exceptional.

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 pack essential information: what the tool does and when to use it. No unnecessary words, front-loaded with the verb and resource. Excellent conciseness.

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 one required parameter, no output schema, and no annotations, the description covers the core purpose, use case, and return fields. It is complete enough for an agent to decide when to invoke it, though it could mention result count or format. Score 4.

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% (one parameter with description). The description adds example keywords ('stroller', 'peloton', 'infant formula') which provide practical guidance beyond the schema's generic description. This adds marginal value, so score 3 (baseline for high coverage).

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 it searches active consumer product recalls by keyword, listing specific agencies and return fields. It distinguishes from siblings like check_in_stock and check_scalper_price by specifying recall-related functionality.

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 explicitly states when to use: 'when an agent needs to know if a product... has been recalled before recommending or buying it.' It does not mention alternatives or when not to use, but the context of sibling tools makes the use case clear, earning a 4.

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 full burden. It describes the comparison against 'current retail across tracked specialty shops' and the return values, but does not disclose limitations like data freshness, scope of shops, error handling, or behavior when price is omitted. This is adequate but incomplete.

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 effective sentences plus a usage guideline sentence. Every sentence is meaningful and front-loaded, with no wasted words.

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?

The description covers the return values (verdict, price band, where to buy) despite no output schema. It mentions all parameters. Missing details about error cases or data freshness prevent a perfect score, but overall it is sufficiently complete for the tool's niche.

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 explains each parameter. The description adds value by listing example products for the niche parameter and clarifying that price is 'the asking price you're seeing'. This enriches understanding 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?

The description clearly states the tool checks if a hobby item's price is fair or scalped by comparing against retail prices. It specifies supported niches with examples, distinguishing it from sibling tools like check_in_stock or find_settlements.

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 explicitly says 'Use when an agent or user wants to know if a... item is overpriced.' This provides clear context for when to use. It does not explicitly state when not to use or list alternatives, but the context is sufficient given sibling tool diversity.

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 provided, so description carries full burden. States it 'returns settlements' with fields but does not disclose if it's read-only, auth needs, or limitations. Adequate for a simple search tool.

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 core purpose. Each sentence adds value: function, return values, usage context. No fluff.

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?

Simple tool with one optional param and no output schema. Description covers purpose, return fields, and use case. Could mention scope (e.g., region) but not needed for clarity.

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 clear description for the optional 'query' parameter. Tool description does not add extra semantic info beyond schema, 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?

Clear verb and resource: 'Find open class-action settlements.' Specifies what is returned (payout, deadline, proof, URL). Siblings are in different domains, so no confusion.

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 use case: 'Use when an agent wants to surface free-money / consumer-rights opportunities.' No when-not or alternatives, but siblings are distinct, reducing ambiguity.

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. It discloses the critical behavior that the analysis runs client-side and is not server-callable, but does not mention other behaviors such as error handling, privacy implications, or what happens with invalid inputs.

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 concise: two sentences that front-load the purpose and immediately follow with the critical limitation. Every sentence is necessary and there is no extraneous 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?

Given no output schema and no annotations, the description adequately covers the tool's purpose, its limitation (client-side analysis), and what it returns (guidance and a link). It is complete enough for an agent to understand the tool's role, though it could mention expected output structure or error scenarios.

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 schema has one optional parameter (image_url) with clear description. The tool description adds value by explaining that the parameter is merely informational because the analysis runs in-browser, which is beyond the schema's description.

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 tool estimates centering and rough PSA-style grade from a photo, but then clarifies that the actual analysis runs client-side, making the tool's direct purpose more about providing guidance and a link rather than performing the estimation itself.

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 does not provide any guidance on when to use this tool versus its siblings (check_in_stock, check_recall, etc.), which are unrelated. It only notes a limitation (not server-callable) but no explicit 'when-to-use' or 'when-not-to-use' advice.

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