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

Description discloses a per-request cost ($0.02 USDC), which is a critical behavioral trait. However, with no annotations, it fails to clarify if the tool is read-only or destructive, or what the response contains.

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?

Description is very short (one sentence plus pricing), but could be better structured with an explicit action and expected output format. Still, it wastes no 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 no output schema, the description should explain what the tool returns (e.g., text analysis, data, report). It does not, leaving the agent unaware of the output structure.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the description adds minimal value beyond the schema. It mentions topics (studio potential, streaming) that align with scope enum values but provides no extra syntax or format details.

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 identifies tool as a Japan anime industry investment guide covering specific areas, distinguishing it from siblings like 'japanese_research' and 'asia_market_data'. However, it lacks a clear action verb like 'get' or 'analyze'.

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?

No guidance on when to use this tool vs alternatives like 'japanese_research' or 'asia_intelligence'. Does not mention excluded scenarios or prerequisites.

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 full burden. It mentions a cost ($0.02 per request) but does not disclose other behavioral traits like required permissions, rate limits, or read-only nature. The description is minimal for a tool that generates intelligence.

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 extremely concise, consisting of a single sentence that conveys the core purpose and cost. It is front-loaded with the most important info but could benefit from slightly more structure.

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 lack of annotations and output schema, the description is insufficiently complete. It does not explain what the output looks like, how to interpret results, or when to choose this over 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 100%, so parameters are fully described in the schema. The description's mention of 'trade policy' aligns with the enum values but adds no new meaning beyond the schema's 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 it provides 'trade policy intelligence and cross-border investment opportunities' for China/Taiwan/Japan, which conveys a clear purpose of delivering analytical insights. However, it does not differentiate from sibling tools like asia_news_api or asia_market_data, which could also cover some of these regions.

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?

No guidance is provided on when to use this tool versus alternatives. The description lacks context for appropriate scenarios, such as when deeper analysis is needed compared to raw market data or news.

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 full burden. It mentions a cost of $0.01 USDC per request, which is a behavioral trait. However, it does not disclose whether the operation is read-only, any side effects, rate limits, or data freshness. The pricing is the only behavioral detail.

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 concise sentences: one for purpose, one for pricing. No extraneous information. Easily scannable.

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 two parameters and no output schema, the description is adequate but incomplete. It does not mention data range, update frequency, or region specifics (though schema covers region values). The pricing is a useful addition. For a simple tool, this is minimally viable.

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 parameters. The description adds value by listing example indicators (GDP, indices, interest rates), which hints at valid values for the 'indicators' parameter. However, it does not add meaning 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 it provides macro economic data (GDP, indices, interest rates) for AI quantitative agents. It distinguishes from sibling tools like news or currency APIs. However, the verb is implicit ('data' rather than 'fetch' or 'get') and the tool name is generic.

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?

No explicit guidance on when to use this tool versus alternatives. The description implies it is for quantitative agents, but does not compare with siblings or indicate scenarios where it is preferred.

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 states the tool provides real-time data and mentions pricing, but lacks details on rate limits, authentication, error handling, or the exact format of the response.

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 long, front-loading the essential purpose and pricing. Every phrase adds value without 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 tool has 5 parameters and no output schema, the description is moderately complete. It explains the tool's domain and pricing but omits return value details, pagination, or error handling, which would be helpful.

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 description coverage, the baseline is 3. The description adds minimal value beyond the schema, only confirming the coverage regions. No additional parameter clarification is provided.

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 retrieves real-time Asian news headlines and summaries, specifying coverage of China, Japan, Taiwan, and Korea. It distinguishes from sibling tools like japanese_research or china_silver_economy by focusing on general news across regions.

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 obtaining Asian news but provides no explicit guidance on when to use this tool versus alternatives like asia_market_data or japanese_research. No conditions or exclusions are mentioned.

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 must disclose behavioral traits. It mentions the cost ($0.03 per request) and domain matching, but fails to cover auth needs, rate limits, idempotency, error handling, or return structure.

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 followed by pricing, both front-loaded and relevant. No superfluous information; every part 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?

Given no output schema, the description should at least hint at what the tool returns (e.g., translated text). It also omits error handling, encoding, length limits, and result format, making it incomplete for an agent to fully understand usage.

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 is 3. The description adds context that domain terminology matching is performed, explaining the purpose of the 'domain' parameter beyond its 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 clearly states the tool performs CN-JP translation focused on patent and regulatory documents with domain terminology matching, distinguishing it from sibling tools which are data/info 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?

The description lacks explicit guidance on when to use this tool versus alternatives. It only implies usage for CN-JP patent/regulatory translation, without stating when not to use it or mentioning other translation options.

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 bears full responsibility for behavioral disclosure. It mentions a cost of '$0.03 USDC per request,' which is a useful trait. However, it does not clarify if the tool is read-only, if it has rate limits, or if it requires authentication, leaving gaps in transparency.

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 short (two sentences) and front-loads the core purpose. The second sentence adds a practical cost detail. While more structure could help, it is efficient and avoids extraneous content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description should explain what the tool returns. It only says 'analysis' without specifying format (text, data, etc.). The parameter usage is not elaborated beyond the schema, and the description lacks completeness for a tool with two parameters and no 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 description coverage is 100%, so the baseline is 3. The description adds minimal extra meaning beyond the schema—it only implicitly connects the focus areas to the enum values. No additional details are given for the 'city' parameter, which the schema already describes as a 'Specific city or region.'

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 'China property market rebound analysis covering policy effects, investment opportunities, and risk assessment for 2026.' It specifies the verb 'analysis' and resource 'China property market rebound.' While it distinguishes from general tools like 'asia_news_api', it does not explicitly differentiate from closely related siblings like 'china_silver_economy' or 'lying_flat_report', but the focus on 2026 rebound is specific enough.

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 this tool is for analyzing the China property market rebound in 2026, but it does not provide explicit guidance on when to use it versus alternatives. No when-not conditions or sibling comparisons are mentioned, leaving the agent to infer usage from context.

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

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

With no annotations, the description carries the full burden. It discloses a cost ($0.02 USDC per request) which is a behavioral trait, and mentions coverage areas. However, it does not explain data freshness, update frequency, or output format, leaving gaps in transparency.

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 plus cost, front-loaded with key information. Every word is purposeful with no redundancy, making it highly concise and well-structured for an agent to quickly grasp the tool's purpose.

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 tool with one parameter and no output schema, the description is reasonably complete. It covers the domain, target demographic, and cost. Minor omission: no indication of return format or data source recency, but overall sufficient for basic 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 100% with the 'sector' parameter fully defined (enum values, default, description). The description does not add any additional meaning beyond what the schema already provides, so the 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 provides market analysis for China's senior population (297 million), covering consumption patterns, healthcare, and trends. It differentiates from sibling tools which focus on general Asia news or other specific topics, making the purpose distinct.

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?

No explicit guidance on when to use this tool versus alternatives. The description implies use for senior market queries but does not specify contexts or exclusions, leaving the agent to infer 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?

With no annotations, the description bears full responsibility for behavioral disclosure. It mentions real-time updates and cost per request, but does not clarify whether the tool is read-only, requires authentication, has rate limits, or what side effects (e.g., billing) occur. These gaps hinder an agent's understanding of safe invocation.

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 extremely concise: a single sentence and a cost note. Every piece of information is relevant, and the purpose is stated upfront. No unnecessary words or repetition.

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 that there is no output schema, the description should have explained return format or structure, but it does not. For a simple tool with two parameters, the missing output details and lack of error/behavioral notes make it incomplete for an agent to fully trust and use it 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?

Input schema has 100% description coverage for both parameters (base and target), each with explicit enums and defaults. The description adds no additional parameter-level meaning 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 it provides real-time exchange rates for three specific currencies (USD/CNY/JPY), specifies its target audience (AI trading agents), and includes a cost note. This distinguishes it from sibling tools like asia_news_api or japanese_research, which do not cover currency exchange.

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 vs alternatives, nor does it mention conditions under which it should not be used. While the sibling tools are mostly news or market data, the description lacks explicit usage context.

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

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

With no annotations, the description carries the full burden. It adds value by disclosing the cost ($0.02 USDC per request) but fails to mention whether the tool is read-only, data freshness, pagination, or any side effects. The behavioral disclosure is minimal.

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 with a clear list of topics and cost, which is efficient. It front-loads the core purpose. However, it could be slightly more structured (e.g., separating content from cost) to improve readability.

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 two simple parameters and no output schema, the description provides enough context to understand the tool's focus but lacks details about output format, result limits, or how to interpret data. It is adequate but not thorough for an AI agent to fully anticipate behavior.

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 baseline is 3. The tool description does not add any additional semantics beyond the schema's parameter descriptions (e.g., 'Days to look back (1-7)' and focus enum). No extra context is provided.

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 explicitly lists specific content types ('Nikkei news, corporate earnings surprises, BOJ policy signals, and sector rotation analysis'), which are distinct from sibling tools that cover broader Asian markets or specific topics like currency or real estate. The verb 'research' is implied, making the tool's purpose clear.

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?

No guidance is provided on when to use this tool versus siblings like 'asia_news_api' or 'cs_jp_currency_api'. There is no mention of when not to use it or prerequisites, leaving the agent to infer from the content 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?

No annotations provided, and the description lacks behavioral details such as result format, rate limits, or whether it makes external API calls. Cost note is the only extra information.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

One sentence plus cost note – very concise with no extraneous text. Could be more informative without sacrificing brevity.

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 niche tool with no output schema and no annotations, the description is too sparse. Missing details on what kind of analysis, output format, and use cases.

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 100% of the single parameter, and the description adds no extra meaning beyond the schema's 'Investment focus or specific area' – baseline 3.

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's purpose: investment analysis of Tokyo's nightlife/entertainment district, distinguishing it from sibling tools focused on other Asia/Japan topics.

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?

No guidance on when to use this tool vs. alternatives. Only mentions cost but no exclusions or prerequisites.

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, but description discloses cost ($0.02 USDC per request) and implies read-only analysis; no mention of authentication, rate limits, or data sources.

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 with cost note; concise but could be better structured (e.g., separate cost line).

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?

No output schema; description does not specify return format, data structure, or example response, leaving significant gaps for a report-generating 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 enum parameter; description adds context on impacts but does not map directly to enum values, providing minimal added 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 analyzes the lying-flat phenomenon and its impacts on consumer behavior, birth rate, and economic implications, distinguishing it from sibling tools like asia_news_api or china_real_estate_2026.

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?

No guidance on when to use this tool versus alternatives; lacks context for selecting among sibling Asia-focused tools.

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