ANIME INTELLIGENCE

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds meaningful behavioral context beyond these: the tool costs 0.02 USDC via x402, uses canonical identity, official references, MSRP relationships, and matched listing signals, and is an advisory risk assessment rather than a transaction. No contradiction with the annotations.

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 three concise sentences with the core purpose front-loaded, followed by methodology, cost, and targeted use cases. Every sentence adds distinct value and there is no filler or repetition of the schema.

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 supplies purpose, method, cost, and explicit trigger conditions, and an output schema is present so return values do not need to be described. It could be slightly stronger by naming sibling alternatives or stating limitations, but overall the description is complete enough for an agent to select and invoke the tool correctly.

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 67%, with query and id described but lang lacking a description in the schema. The description contributes contextual meaning by naming the signal sources used in assessment, but it does not explain the lang parameter or add formats beyond the schema's examples. This is adequate but not a strong compensation for the coverage gap.

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 opens with a specific verb and resource: 'Assess counterfeit, bootleg and suspicious-listing risk' for Japanese anime figures. This clearly distinguishes it from siblings like identify_anime_product or anime_market by focusing on authenticity risk rather than identification, pricing, or purchase timing.

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 gives explicit trigger conditions: 'Use when the user is worried about bootlegs, suspiciously cheap listings, missing official references or risky marketplace offers.' It also positions the tool before a recommendation or purchase. It does not explicitly name sibling alternatives or when-not-to-use cases, so it stops short of a 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 mark the tool as read-only, idempotent, open-world, and non-destructive. The description adds valuable non-obvious behavior: it costs 0.02 USDC via x402, returns a decision plus purchase routing, and incorporates multiple signal types. No contradiction with annotations.

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, each contributing distinct information: the core output, the purchase-routing behavior and cost, and the triggering user intent. It is front-loaded and free of filler.

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 purpose, inputs, cost, and when to use the tool, and an output schema exists so return values need not be spelled out. It is nearly complete, though it could briefly state that raw market/rarity/authenticity details belong to sibling tools.

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 67%, with id, query, and lang already described in the schema. The description mentions analytical inputs (price, MSRP, rarity, authenticity) but does not add detailed meaning for the actual parameters beyond what the schema provides, so a 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?

The description states a specific output ('Return BUY, WAIT, WATCH or AVOID') for a clearly bounded resource ('an identified anime collectible'). This distinguishes it from sibling tools like anime_market or anime_rarity, which provide raw data rather than a recommendation.

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 gives an explicit trigger: 'Use when the user asks whether to buy now, wait for a lower price, watch the market or avoid the item.' It does not explicitly list when not to use it or name alternative sibling tools, but the use case is clear.

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 mark this as read-only, idempotent, open-world, and non-destructive, and the description does not contradict them. It adds valuable behavioral context beyond the annotations: the result includes lowest/median/highest price, offer count, freshness, and best matched listing, that it rejects likely identity mismatches, and that it costs 0.01 USDC via x402.

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, each earning its place: purpose/scope, return summary and cost, then usage timing. The most important information is front-loaded, and there is no repetitive or filler 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 tool with an output schema and read-only annotations, the description is largely complete: it states prerequisite, cost, data sources, and the shape of the returned intelligence. It could be more complete by naming sibling alternatives or giving explicit when-not-to-use conditions, but these gaps are minor.

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 already documents query and id, and the anyOf structure conveys that one of them is required, while lang has an enum/default. The description does not add any parameter-level guidance beyond saying the item is 'identified', so it provides no additional semantic value for choosing or formatting parameters.

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 opens with a specific verb and resource: 'Get current matched asking-price intelligence for an identified Japanese anime collectible', and names data sources (Yahoo Shopping, eBay, stored market observations). It also differentiates itself from identify/search siblings by restricting use to an already identified item and by enumerating the price summary outputs. This is unambiguous and not a tautology.

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 states when to use the tool: 'Use after identity is known' and when the user asks for current price, market range, cheapest observed listing, or current availability. It does not explicitly name alternative sibling tools or give 'when not to use' exclusions, so it stops short of a 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 readOnly, openWorld, idempotent, and non-destructive hints, so the description only needs to add value beyond those. It adds important behavioral context: the tool costs 0.01 USDC via x402 and produces an estimate based on matched supply, price premium, release age, limited-edition metadata, and rerelease information. It does not detail output shape, but the presence of an output schema lowers that burden.

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 three concise sentences with no filler. It front-loads the core action, then the price, then the usage triggers. Every sentence adds useful information for selecting and invoking the tool.

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 tool with an output schema, safety annotations, and a short input schema, the description is nearly complete. It communicates cost, methodology, and clear invocation triggers. The only minor gap is that it does not explicitly say when to prefer a sibling tool such as anime_authenticity or anime_market, though the usage list makes the boundary reasonably clear.

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 already describes 'query' and 'id', and 'lang' is a self-explanatory enum with a default. With schema description coverage at 67%, the description does not need to repeat parameter details. It mentions high-level inputs (supply, premium, age, metadata) but does not map them directly to parameters, which is acceptable given schema 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 states a specific action ('Estimate scarcity and rerelease risk') on a specific resource ('Japanese anime collectible') and lists the signals used. It also clearly frames the intended user questions ('rare, hard to find, limited, likely to appreciate, or exposed to rerelease risk'), which helps distinguish it from sibling tools like anime_authenticity or anime_market.

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 gives an explicit 'Use when the user asks whether...' list, covering the main cases where this tool should be chosen. It does not name alternative sibling tools or provide when-not-to-use guidance, but the usage trigger is specific enough for an agent to route correctly in most cases.

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 signal read-only, idempotent, non-destructive, open-world behavior. The description adds useful beyond-annotation context: the result is based on current observations, ranked by identity match quality, total price, seller information, and freshness, and the call costs 0.03 USDC via x402. No contradiction with annotations.

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 with no filler. The core function and ranking priorities are front-loaded, then the price and exact use trigger are given. 'purchase route' appears twice, but the second occurrence serves as the routing trigger rather than redundancy.

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 rich annotations, an output schema, and a simple 3-parameter input schema, the description is complete for selecting and invoking the tool. It provides cost, when-to-use, and what the result optimizes; nothing necessary 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?

With 67% schema coverage, the schema already documents query and id, and lang has an enum/default. The description adds minimal parameter-specific meaning: it calls the product 'identified,' aligning with the id/query requirement, but does not explain lang or the id-vs-query choice beyond the schema's anyOf.

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 opens with a specific verb and resource: 'Find the best currently observed purchase route for an identified Japanese anime collectible across supported marketplace observations.' It names the ranking priorities and clearly distinguishes itself from search/identification siblings by focusing on purchase routing.

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 second sentence gives explicit usage conditions: use when the user has decided to buy and asks where to buy, for the cheapest matched listing, or for the best purchase route. It implies 'not when undecided or asking whether to buy,' but it does not name sibling alternatives directly.

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 readOnly, openWorld, idempotent, and non-destructive behavior. The description adds valuable operational context beyond annotations: it costs 0.05 USDC via x402, and it aggregates multiple decision dimensions into one recommendation. This transparency about cost and scope is especially important for an autonomous agent.

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 compact and front-loaded with the core value proposition, then adds cost and usage guidance. There is mild redundancy in listing the package components twice, but every sentence provides useful information. It is appropriately sized for a high-value aggregate endpoint.

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 output schema exists, so return value details are not required in the description. The description covers what the tool does, when to use it, what it costs, and how it relates to sibling endpoints. Nothing essential is missing for correct invocation and selection.

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 already describes id and query with useful examples, and lang has an enum. The description adds no parameter-level detail beyond the schema, but with 67% schema coverage, the schema carries most of the burden. A neutral score is appropriate because the description does not need to repeat schema content.

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 defines the tool as a complete decision package for a Japanese anime collectible, enumerating the exact components: identity, market, rarity, rerelease status, authenticity risk, BUY-WAIT decision, and purchase route. It differentiates itself from sibling tools by framing itself as the highest-value, aggregate endpoint. The purpose is unmistakable and specific.

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 the tool: when the agent needs identity, market, rarity, authenticity, timing, and purchase route together. It contrasts with calling individual endpoints, implying that single-dimension needs should go elsewhere, though it does not name specific sibling alternatives. This is clear enough for an agent to route correctly.

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?

Beyond the annotations (readOnly, idempotent, openWorld, non-destructive), the description discloses the paid nature with exact pricing (0.005 USDC via x402), a critical operational fact. It also explains the resolution process: uses JAN/EAN-13, manufacturer data, aliases and official-source evidence to collapse ambiguous names into a canonical identity. No contradiction with annotations.

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?

Four sentences, front-loaded with the core purpose and followed by evidence-based resolution, pricing, and trigger conditions. Some minor filler like 'for agents that need a reliable product identity' adds limited information, and 'cheapest' is mildly promotional, but overall each sentence contributes to selection or invocation.

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 3-parameter tool with an output schema, the description covers the main gaps: how to invoke (query or id), what input kinds are accepted, when to choose it, and the cost/payment method. The presence of an output schema means return-value explanation is unnecessary. The anyOf structure in the schema handles the query-vs-id choice, and the description reinforces the intended use.

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 67%: query and id have descriptions, lang has only an enum without a textual description. The description compensates for query semantics by enumerating accepted inputs (product name, character, JAN/EAN-13, model number, series name, ambiguous collectible description) and clarifies the goal of canonical product resolution. It doesn't add anything for id or lang, but those are straightforward from property descriptions and enum values.

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 (Identify the exact Japanese anime figure or collectible) and a clear resource (canonical product via JAN/EAN-13, aliases, manufacturer data). It distinguishes itself from siblings logically: says to use it before searching prices or buying, which separates it from search_anime_product and anime_market, and 'cheapest paid entry point for reliable product identity' positions it against full_intelligence.

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?

Provides explicit trigger conditions: 'Use first when the user gives a product name, character, JAN/EAN-13, model number, series name or an ambiguous collectible description.' This gives the agent clear selection criteria. It does not explicitly state when not to use it or name an alternative like search_anime_product, but the 'Use first' ordering and 'before searching prices or buying' strongly imply the routing decision.

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?

There are no annotations, so the description carries the full burden of behavioral disclosure. It only says the search is 'free' and 'canonical' but does not explain what constitutes canonical, how search results are returned, whether partial matches are supported, or any external data source behavior. This is a significant gap for a 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?

The description is two short sentences with no filler. The core purpose is front-loaded, and the usage guidance is concise and actionable. Every word 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?

The description covers the main search purpose, the free/paid positioning, and the condition for use. However, it omits any detail about the lang parameter, does not name the paid sibling tools, and provides no behavioral context about search matching or result characteristics. The output schema helps, but the description alone leaves gaps.

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 only 50%; the query parameter is documented well in the schema, but the lang parameter has no description. The tool description does not explain lang or add parameter-level meaning. It adds context about the search scope but does not compensate for the undocumented lang parameter.

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 ('free canonical search') and resource ('Japanese anime figures and collectibles'), which clearly defines what the tool does. It distinguishes itself from paid tools by noting it is free and should be used first, though it does not name a specific sibling tool like identify_anime_product as the direct alternative.

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 gives explicit usage guidance: 'Use this before paid tools when the exact product is uncertain.' This clearly indicates when to use the tool and implies the alternative is paid tools. However, it does not explicitly state when not to use it or name the specific sibling tools that are the paid alternatives.

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