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?

The description goes far beyond the readOnlyHint/destructiveHint annotations, revealing critical behavioral traits: prices are based on verified real sales (robust median), not listings or interpolations; the tool returns no price when evidence is insufficient rather than fabricating one; and markets are segregated by region/currency (JPY for Japanese cards, CNY for Chinese cards). This level of transparency is highly valuable for an AI 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 front-loaded with the core purpose and efficiently packs methodology and market caveats into three sentences. Each sentence earns its place, though the final market-separation sentence is somewhat dense. It is appropriately concise for the tool's complexity.

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's complexity (multiple currencies, grading companies, confidence/sample size, market-specific pricing), the description comprehensively covers data provenance, honesty behavior, and market logic. An output schema exists, so the description need not enumerate return fields, but it still summarizes the headline and ladder output, making it complete for an AI 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?

The tool description adds little about the card_id parameter beyond restating 'by card_id'. However, the input schema already provides a thorough description (including nickname resolution) with 100% coverage, so the baseline of 3 applies. No additional parameter semantics are needed.

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 provides the 'current representative price for one card' and details the output (headline price with grading company, confidence, sample size, last sold date, plus per-grade ladder). This distinguishes it from siblings like card_price_history (historical price) and market_movers (market-wide trends) by explicitly focusing on a single card's current 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 implies usage for retrieving current single-card prices through the word 'current' and the card_id parameter, but it does not explicitly contrast with sibling tools or state when not to use it. The schema mentions resolving ids via search_card/slang_lookup, but there is no explicit guidance on choosing this over card_price_history or market_movers.

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?

Beyond the readOnlyHint annotation, the description discloses the default window behavior, the statistical confidence filter, and the 20-card limit. These details inform the agent about response size and filtering logic.

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, front-loaded with the main purpose, followed by relevant behavioral details. 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?

With an output schema present and all parameters documented in the schema, the description fills in behavioral context such as default windows and confidence filtering. It is sufficient 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?

The schema fully covers all three parameters, but the description adds valuable semantics for the window parameter (default behavior, thin tracks use longer windows). Direction is not elaborated, but the schema description covers its meaning.

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 top gainers or losers among graded Pokémon cards by market track. This differentiates it from siblings like market_volume_leaders, which focuses on volume.

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 explains that window defaults to the track's published default and can be overridden, and notes that only statistically confident moves are listed. While it doesn't name alternative tools, it gives clear contextual guidance for when 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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so no need to restate safety. The description adds valuable behavior: 'Returns up to 10 matching cards' and 'tracks are never mixed', giving the agent expectations about result limits and language isolation. No contradictions 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?

The English portion is front-loaded with the main action and key examples. The Chinese sentence is redundant for many users but serves the tool's Chinese-speaking audience, so it contributes value. Overall structure is logical, though slightly long.

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 an output schema exists (per context signals), the description doesn't need to explain return format in detail, but it already mentions the 10-result cap and card_id usage. It covers accepted input formats, language filtering, and next-step usage, making it complete for a search tool with 2 params.

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 both 'query' and 'lang' described. The tool description adds extra meaning by giving concrete query examples (e.g., 'sv8a 喷火龙') and explicitly explaining the lang enum effect ('tracks are never mixed'). This goes beyond the schema's terse parameter 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 states a specific verb+resource: 'Search graded Pokémon cards' and enumerates exact search input types (name, nickname, set code + number). It distinguishes itself from sibling tools by explicitly saying 'use card_id with the other tools', establishing it as the entry-point search tool.

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 concrete examples ('sv8a 喷火龙') and clarifies that the result is a card_id meant for downstream tools. It implies when to use this tool (when you need card_id) without explicitly naming alternatives, but the context is clear from the sibling list. It also gives a usage constraint: 'lang filters to one language track; tracks are never mixed'.

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.