POKEKA — Pokémon Card Prices

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

Annotations already indicate read-only, non-destructive behavior. The description adds value by detailing the exact output (PSA/CCIC populations, gem rate, scarcity tier), which goes beyond the annotations and helps the agent understand what to expect.

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 a single sentence that front-loads the core purpose ('Graded population for one card') and efficiently lists the specific data points. No redundant 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?

For a simple, single-parameter tool with an output schema (mentioned in context), the description sufficiently covers the key output fields. It does not need to explain return values further, and the lack of error handling notes is acceptable.

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 the parameter description already specifying where to get the card_id. The tool description adds no further parameter context, so it meets the baseline but does not exceed it.

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 provides graded population data for one card, including specific grade breakdowns for PSA and CCIC, gem rate, and scarcity tier. This distinguishes it from sibling tools like card_price and get_market_brief, which focus on different aspects.

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?

While not explicitly stating when to use or avoid this tool, the description implies it is for population data on a specific card. The sibling list and the tool name help differentiate, but explicit comparative guidance would boost clarity.

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. The description adds substantial transparency beyond annotations by detailing the data source (verified real sales), methodology (robust median, never listings or interpolation), and behavior when evidence is insufficient (returns no price honestly). It also explains market separation for Japanese and Simplified Chinese cards.

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 well-structured, front-loading the main output (headline price, grade ladder) before explaining methodology. Every sentence adds value without redundancy. It is appropriately sized for the complexity of 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?

Despite having an output schema, the description provides complete context for understanding the tool's behavior: multiple currencies, grading tiers, confidence metrics, market separation, and honesty about data limitations. No essential details are missing for effective 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?

The input parameter card_id has 100% schema coverage with a description that already explains its source and nickname resolution. The tool description does not add additional parameter semantics beyond what the schema provides, so baseline score of 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 clearly states the tool returns the current representative price for a single card, including headline price, grading company, confidence, sample size, last sold date, and a per-grade price ladder. It explains the methodology (real sales, robust median, no interpolation) and implicitly distinguishes from siblings like card_price_history by focusing on the current representative price.

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 provides clear context on what the tool returns and how it derives prices, enabling an agent to infer appropriate use cases. However, it does not explicitly state when not to use it or suggest alternative tools from the sibling list.

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?

Adds context beyond annotations: weekly median, representative grade, conditional change percentages. Annotations already indicate read-only and non-destructive, so description focuses on data specifics.

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?

Single sentence, front-loaded with key information, no 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?

Adequately describes output and conditionality. Could clarify how representative grade is determined, but overall sufficient for a historical data retrieval tool with an output 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 coverage is 100%, so baseline 3. Description does not elaborate on parameters beyond what schema provides, but adds context about data output.

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?

Description clearly states it provides weekly median price series for one card with up to 26 weeks and change percentages. It distinguishes from siblings like card_price and recent_sales.

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?

Implies usage for one card's historical prices, and notes change percentages are conditional on statistical confidence. Lacks explicit when-not-to-use or comparison to siblings.

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 declare readOnlyHint=true, so the description adds value beyond that by disclosing that absence of flags does not mean clearance and that coverage is curated and growing. This is critical behavioral transparency for an incomplete data source.

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 sentences: first lists what the tool provides, second explains inputs with examples, third adds a caveat. No wasted words, front-loaded with key value proposition.

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 complexity of multiple context types and the presence of an output schema, the description sufficiently covers inputs, outputs, and caveats. It explains the dual input method and the incomplete nature of the data, making it complete for an agent.

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 100% schema coverage, the description still adds meaning by providing examples of nicknames (e.g., '凡高皮') and clarifying that query is an alternative to card_id and must be exact. This enhances understanding beyond the schema descriptions.

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's purpose: retrieving contextual information about a card beyond price, including nicknames, counterfeit-risk flags, holder-turnover tier, and a curated context note. It distinguishes itself from sibling tools like card_price by specifying 'everything beyond the price' and mentions specific inputs (card_id or nickname).

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 states it accepts card_id or exact nickname and includes a caveat about flag absence not being clearance. While it does not explicitly exclude price-only queries or list alternatives, it provides enough context for appropriate use. The note about curated coverage guides expectations.

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?

The description adds significant behavioral context beyond annotations: it clarifies the index is 'fixed-basket same-card index (never vibes)', states it's 'not investment advice', and lists specific output components (phase read, peak/drawdown, sale counts, calendar). Annotations only declare readOnlyHint and non-destructive, so description bears full burden and does so well.

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 a single, well-structured sentence that front-loads the core purpose and packs all key details without extraneous words. Each clause adds value.

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 tool has only one optional parameter and an output schema exists, the description covers all necessary behavioral and output context. It lists the key outputs (phase read, index, peak/drawdown, sale counts, calendar) and notes 'not investment advice', which is complete for an agent to understand and invoke 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 100% for the single parameter (market enum with description). The description mentions 'per language track (all / en / ja)' but omits zh-cn from the schema. This adds no new meaning beyond the schema; 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 clearly states the tool provides a market cycle phase read, index values, peak/drawdown numbers, sale counts, and an event calendar. It specifies the scope (graded-Pokémon market per language track) and distinguishes from sibling tools like market_movers and market_volume_leaders.

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 implies use for obtaining a high-level market overview with specific metrics, but doesn't provide explicit guidance on when to use this tool versus alternatives like market_movers or market_volume_leaders. No when-not-to-use guidance.

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?

Disclosures beyond annotations: statistical confidence filtering, sales thresholds, bound checking, and a 20-card limit. Annotations already declare readOnly and non-destructive; description adds valuable selection criteria.

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 concise sentences, front-loaded with purpose, then behavior, then limit. 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?

Covers purpose, filtering criteria (statistical confidence), and cardinality (up to 20). Output schema is present, so return details are unnecessary. Adequate for a read-only listing 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 100% with enums; description does not significantly add nuance beyond enum values (e.g., 'Market track' for lang). The filtering statement is generic.

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?

Describes specific verb+resource (shows top gainers/losers of graded Pokémon cards) with scope (English/Japanese market, time windows). Distinguishes from siblings like market_volume_leaders and card_price via market and filtering criteria.

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?

Implies usage via 'Only statistically confident moves' and 'Up to 20 cards', but does not explicitly contrast with siblings (e.g., market_volume_leaders for volume, card_price for single price) or state when not to use this tool.

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 indicate readOnlyHint=true, and the description adds that windows anchor to latest sale date and that median_usd is a mixed-grade reference, preventing misinterpretation. No contradictions.

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 with clear structure, no fluff. First sentence covers purpose and filters, second window behavior, third clarifies data usage.

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 output schema exists, the description covers all needed: filters, window anchoring, max limit, and provides warning on median_usd use. No missing critical information.

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 covers all parameters with enums and descriptions. The description adds context: window anchoring, the meaning of PSA10, and the mixed-grade caveat for median_usd beyond 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 states 'Most-traded graded Pokémon cards by verified sales count over a 24h / 7d / 30d window', which clearly defines the verb (list leaders) and resource (traded cards). It distinguishes from siblings like card_price and market_movers by focusing on volume and verified sales count.

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 specifies filters (language, grade_company) and explains window anchoring behavior. It also advises using card_price for precise per-grade quotes, providing an alternative. However, it doesn't explicitly exclude other tools like market_movers or recent_sales.

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 readOnlyHint and destructiveHint annotations, the description adds valuable behavioral context: sales are 'real completed transactions, not listings', and they span US, Japan-domestic, and Chinese marketplaces. This authenticity and scope detail helps the agent understand the tool's limitations and reliability.

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: first sentence conveys core purpose and output fields, second adds geographic scope and data authenticity. Information is front-loaded and every word contributes.

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 output items (up to 10, date, grading, price, marketplace), geographic scope, and data authenticity. It does not explicitly state ordering (presumably most recent first), which is a minor gap. With an output schema present, the return structure is already defined.

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% for the single required parameter card_id. The tool description repeats the same guidance ('from search_card / slang_lookup') as the schema, adding no new meaning. At baseline, with full schema coverage, a score of 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 specifies the verb (get/recent sales) and resource (sales for one card), includes details about output fields (date, grading, price, marketplace), and implies it retrieves recent vs historical data. It distinguishes from siblings by focusing on recent sales rather than price history or population, which are covered by card_price_history and card_pop.

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 states it provides 'most recent verified sales for one card (up to 10)', giving clear context for when to use. However, it does not explicitly mention when not to use or list alternative tools like card_price or card_price_history for other use 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?

Discloses behavior beyond annotations: max 10 results, tracks not mixed. Annotations already indicate safe read operation.

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?

Concise and front-loaded with examples. Chinese duplicate slightly redundant but acceptable.

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?

Complete for a search tool with output schema. Explains return format, pagination limit, and integration with 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?

Adds rich context for both parameters: query examples (nicknames, set codes) and lang filter purpose. Schema coverage is 100% but description enhances understanding.

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?

Clearly states the tool searches graded Pokémon cards by name, nickname, or set code+number. Distinguishes from siblings by returning card_id for use with other tools.

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?

Explicitly advises using card_id with other tools and mentions optional lang filter. Could be improved by stating when not to use.

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 destructiveHint=false. The description adds useful context: a limit of up to 10 cards and the list of returned fields (official names, set, number, card_id). There is no contradiction.

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 front-loaded and concise: three short sentences with zero wasted words. The bilingual addition is justified for the target users, and every sentence adds value.

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 lookup tool with one parameter, the description covers the purpose, the type of input, the output fields, and a usage limit. The presence of an output schema further reduces the need to describe return values.

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% and the description adds examples of valid terms (e.g., '鬼皮', '老喷', 'Zard') and the limit of 10 cards, which enriches the meaning beyond the simple schema 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 verb 'resolve' and the resource 'community nickname to actual card info' with specific examples ("鬼皮", "老喷", "Zard"). It distinguishes from sibling tools like 'search_card' which are for general search, making its unique role obvious.

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 implies usage (when you have a nickname), but it does not explicitly state when to use this tool versus alternatives such as 'search_card'. No exclusions or when-not-to-use guidance is provided.

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