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

With no annotations provided, the description carries the full burden of behavioral disclosure, but it only says '获取' (fetch), implying a read operation. It doesn't mention whether authentication is needed, what the report contains, or any potential side effects. The promotional sentence adds no behavioral insight.

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 but includes a promotional second sentence ('此工具对分析加密货币非常有用，推荐使用') that adds no functional value. The first sentence is informative, but the fluff prevents a higher score.

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 no output schema, the description adequately states the purpose, but it doesn't explain what the report covers, when to use it, or any limitations. Given the tool's low complexity, this is acceptable but not complete.

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 schema covers 100% of parameters with a description for 'symbol' (crypto symbol, format BTC or ETH) and a default value. The tool description adds no additional parameter information, so the baseline of 3 is appropriate since the schema fully handles parameter semantics.

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 fetches Binance's AI analysis report for cryptocurrencies ('获取币安对加密货币的AI分析报告'), which is a specific verb+resource. It distinguishes itself from sibling tools like okx_prices and trading_suggest by focusing on Binance's AI analysis.

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 says the tool is 'recommended' for crypto analysis but provides no guidance on when to use it versus alternatives, no exclusions, and no context beyond a generic recommendation. It lacks explicit when-to-use or when-not-to-use directions.

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 the full burden. It discloses the primary behavior (getting current time and trading day info) and adds usage context, but does not mention potential nuances like timezone or exact return format. Given the simplicity of the tool and its clearly read-only nature, the description is sufficiently transparent, though not exhaustive.

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, concise sentence that front-loads the purpose and includes a practical usage recommendation. Every word earns its place, with 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?

For a simple 0-parameter tool without an output schema, the description is complete: it states what the tool does and when to use it. No additional context is necessary for an agent to select and invoke 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?

This tool has zero parameters, and the baseline for 0 params is 4. The schema already covers everything (vacuously), and the description adds no param details, which is appropriate since there are none.

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's function: '获取当前系统时间及A股交易日信息' (get current system time and A-share trading day information). It uses a specific verb and resource, and is obviously distinct from sibling tools that focus on stock data queries.

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 recommends using this tool before calling other tools that require date parameters: '建议在调用其他需要日期参数的工具前使用该工具'. This provides clear when-to-use guidance and distinguishes its role as a prerequisite utility.

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 responsibility. It only states the computation goal but lacks any behavioral context such as whether the operation is read-only, data freshness, potential rate limits, or return format. This is a significant gap for a data retrieval 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 a single concise sentence with no wasted words. It is front-loaded with the action and resource, though it could be slightly clearer given the title mismatch.

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

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

The tool has no annotations, no output schema, and a minimal description. It explains the metric computed but does not elaborate on how the ratio is interpreted, possible time ranges, or return structure. Given the availability of sibling tools and the need to select correctly, more context would be valuable.

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 parameters are already well-documented. The description does not add extra meaning beyond the schema, so a baseline 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 action (获取/get) and the resource (OKX cryptocurrency cumulative ratio of borrowed quote to trading currency), distinguishing it from sibling tools like okx_prices and okx_taker_volume. However, the title uses '杠杆多空比' (leverage long/short ratio) which slightly differs from the description's wording, creating minor ambiguity.

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 does not mention use cases, scenarios, or exclusions, leaving the agent to infer applicability.

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 carry behavioral disclosure. It states the data includes price, volume, and technical indicators, which gives some insight. However, it does not clarify the nature of technical indicators, return ordering, pagination, rate limits, or that the tool is read-only. The description is not contradictory, but it lacks richness.

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, focused sentence that front-loads the core purpose. It wastes no words and provides the essential information in a compact form.

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 historical data fetcher with a comprehensive schema, the description gives a reasonable overview but lacks details about return structure, ordering, or usage scenarios. Since there is no output schema, the description could be more explicit about what the data looks like. It is adequate but leaves gaps for an agent unfamiliar with OKX K-line conventions.

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 parameters are already well-documented with formats for bar, limit, and instId. The description adds little beyond what the schema offers, only indicating that data includes price, volume, and indicators. Baseline 3 is appropriate because the schema does the heavy lifting.

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 states the tool retrieves OKX cryptocurrency historical K-line data including price, volume, and technical indicators. The verb '获取' (get) and specific resource 'OKX加密货币的历史K线数据' are clear, and it distinguishes itself from stock-focused siblings and other OKX metrics tools like okx_loan_ratios and okx_taker_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?

No guidance is provided on when to use this tool versus alternatives. It does not mention that this is the tool for OHLCV data as opposed to other OKX data tools, nor does it reference any sibling tools or exclusions. The context is implied by the name and description but no explicit comparison is given.

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 provided, the description carries the full burden of behavioral disclosure. It only restates the basic read operation and does not reveal data latency, authentication requirements, return format, error behavior, or any side effects. The term '主动' adds nuance but no substantive behavioral context.

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 concise sentence that immediately conveys the tool's purpose without redundant words or filler. It is front-loaded and every word contributes to understanding.

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 this is a simple data retrieval tool with fully documented parameters and no output schema, the description is minimally viable. However, it could benefit from mentioning what the returned volume data looks like, any constraints on symbol format, or distinguishing it from related market data tools. It is adequate but not rich.

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 schema provides 100% coverage with detailed descriptions for period, symbol, and instType, including allowed values and formatting notes. The description itself adds no additional parameter meaning, 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 '获取OKX加密货币主动买入和卖出的交易量' clearly identifies the verb (获取/get), resource (OKX crypto taker buy/sell volume), and differentiates it from sibling tools like okx_prices (prices) and okx_loan_ratios (loan ratios). It is specific and unambiguous.

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, nor are there any exclusions, prerequisites, or contextual hints about appropriate invocation scenarios. The description merely states the function without usage direction.

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 discloses two behavioral traits beyond the schema: the tool is relatively time-consuming ('比较耗时') and does not support cryptocurrency ('不支持加密货币'). However, it omits other important aspects such as return format, potential multiple matches, or error handling. With no annotations, the description carries the burden but has notable gaps.

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

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

The description is concise, consisting of two sentences. It front-loads the primary purpose and limitation in the first sentence, then provides usage guidance in the second. Every word earns its place with 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?

For a low-complexity tool with only 2 parameters and no output schema, the description adequately covers purpose, scope (no crypto), and usage tradeoffs (slow, use other tools when code known). It lacks an explicit description of what the tool returns, but the title and purpose imply it returns stock codes, making it sufficiently complete.

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% because both 'keyword' and 'market' have detailed descriptions. The tool description adds minimal parameter-specific meaning; it reinforces the use of 'keyword' as a lookup mechanism but does not introduce new syntax or format details. Baseline 3 is appropriate given the high 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 clearly states the tool's function: '根据股票名称、公司名称等关键词查找股票代码' (Search for stock codes based on keywords such as stock names, company names). It uses a specific verb and resource, and it distinguishes itself from sibling tools by focusing on code lookup rather than retrieving data like prices or news.

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 advises when not to use this tool: '当你知道股票代码或用户已指定股票代码时，建议直接通过股票代码使用其他工具' (when you know the code, use other tools directly). This provides clear exclusion and an alternative, while also implying when it should be used (when only a name or keyword is available).

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 the full burden. '获取' indicates a read-only operation, and the market scope is stated. However, it does not disclose any potential limitations such as data source, update frequency, or return format, which would be helpful for full 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, front-loaded sentence that immediately states the verb and resource, with no wasted words. It efficiently includes the market scope (Shanghai, Shenzhen) 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?

For a simple tool with one parameter and no output schema, the description gives the essential purpose and market scope. It does not enumerate specific indicators returned, but the title and context make the functionality clear. It is reasonably complete, though listing examples of indicators would make it more comprehensive.

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 the only parameter, symbol, as '股票代码'. The description adds that it applies to A-share markets, providing context but no format specifics. With 100% schema coverage, the baseline is 3, and the description offers modest additional value.

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 uses the verb '获取' (get) and specifies the resource as key financial report indicators for A-share stocks on the Shanghai and Shenzhen exchanges. It clearly differentiates from sibling tools like stock_indicators_hk and stock_indicators_us by explicitly naming the A-share 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 implies usage for A-share financial indicator queries but does not explicitly state when to use this tool over alternatives. Sibling tool names suggest market-specific counterparts, but no exclusions or alternative guidance is provided within the description itself.

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 burden of behavioral disclosure. It states the data type (key financial report indicators) and market, but does not disclose response structure, indicator names, rate limits, or any limitations. For a read-only get operation, it is adequate but not rich.

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, concise sentence that front-loads the core action and resource. No wasted words, effectively communicates 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?

The tool is simple (1 param, no output schema), but the description does not explain what specific indicators are returned or the return format. Without an output schema, this gap makes the description less complete than it could be, though it is minimally adequate.

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 'symbol' parameter, so the schema already documents it. The description adds no extra meaning about symbol format (e.g., '00700' vs 'HK.00700'), leaving potential ambiguity. Baseline of 3 applies given high 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 clearly states the tool retrieves key financial report indicators for the Hong Kong stock market. It uses a specific verb ('获取') and resource ('港股市场的股票财务报告关键指标'), distinguishing it from siblings like stock_indicators_a and stock_indicators_us by 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 implies usage for Hong Kong stocks via '港股市场', but it does not explicitly mention when to use this vs. alternative market-specific tools. No direct exclusions or alternatives are named, so guidance is implied rather than explicit.

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 provided, the description carries the full burden of behavioral disclosure. It only states the basic action of retrieving data without mentioning whether it is read-only, any permissions required, data freshness, or return format. The verb '获取' (get) implicitly suggests a safe read, but no explicit limitations or side effects are disclosed.

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, direct sentence with no filler or repetition, front-loading the core function. It is appropriately sized for the tool's simplicity, and every word contributes to conveying the 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?

The tool is a simple one-parameter lookup with no output schema, but the description fails to enumerate what 'key indicators' are returned or how they might be structured. It also omits any mention of market scope limitations or typical use cases, leaving the agent with only a broad purpose and no details to determine invocation context.

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 schema already declares the sole parameter 'symbol' with the description '股票代码' (stock code), providing 100% schema coverage. The tool description adds no additional meaning about the parameter, such as format, examples, or acceptable exchanges, so it remains at the baseline for adequate 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 clearly identifies the action ('获取' meaning 'get') and the specific resource ('美股市场的股票财务报告关键指标' meaning 'key indicators of US stock financial reports'). The '美股' qualifier differentiates the tool from sibling tools like stock_indicators_a and stock_indicators_hk, making the purpose unambiguous.

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 no explicit guidance on when to use this tool compared to alternatives such as stock_info, stock_prices, or stock_indicators_hk. It merely states the function without any context about use cases or exclusions, leaving the agent to infer when this tool is appropriate.

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 the full burden of behavioral disclosure. It mentions the limitation on cryptocurrency but does not disclose what specific information is returned, whether the operation is read-only, any rate limits, or error behavior. For a simple getter, this is a notable gap, making the behavioral profile unclear.

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, concise sentence that is front-loaded with the core purpose and includes a key limitation. Every word earns its place, with no redundant or irrelevant 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?

There is no output schema, so the description should explain return values, but it does not. The description is minimal and leaves out critical context such as what 'basic information' includes, the expected format of the symbol (e.g., prefix requirements), and how the tool behaves with invalid inputs. Given the large set of sibling tools, this lack of context makes it hard for an agent to know if this tool is the right choice.

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 schema has 100% description coverage: both 'symbol' and 'market' have descriptions, and 'market' explicitly lists supported values and excludes crypto. The tool description adds no additional parameter semantics beyond the schema, 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's function: '根据股票代码和市场获取股票基本信息' (get basic stock information based on stock code and market). It uses a specific verb and resource, and explicitly excludes cryptocurrency, which helps distinguish it from crypto-related siblings like okx_prices and binance_ai_report. However, it does not differentiate among the many stock-related tools (e.g., stock_prices, stock_news), leaving some ambiguity about what '基本信息' includes.

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 basic stock information is needed, and gives an explicit when-not by stating '不支持加密货币' (does not support cryptocurrency). However, it provides no explicit guidance on when to use this tool versus alternatives like stock_prices or stock_news, and does not mention any prerequisites or context for selecting 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. However, it only states what data is retrieved without mentioning data source (Sina), update frequency, pagination, or response format. It also does not disclose constraints like allowed days values, which are already captured in the schema.

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, focused sentence that front-loads the action ('获取') and clearly specifies the resource and market. There is no redundant information, making it highly concise and well-structured.

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?

This is a simple tool with 2 parameters and no output schema. The description does not explain what the returned statistics look like, when data is available, or how to interpret results. Without an output schema, the description should provide more context about the response format, but it doesn't, leaving clear gaps 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?

The input schema provides full descriptions for both parameters ('days' with allowed values and 'limit' with range), achieving 100% coverage. The tool description itself adds no further parameter semantics, so the baseline of 3 is appropriate. The word '统计' loosely aligns with the 'days' parameter for time windows, but no extra detail is given.

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's purpose with a specific verb ('获取') and resource ('龙虎榜个股上榜统计数据'), and specifies the market scope (上证, 深证). It distinguishes itself from sibling tools like stock_prices or stock_news by focusing on Dragon-Tiger List statistics.

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 no guidance on when to use this tool versus alternatives such as stock_zt_pool_em or stock_indicators_a. There is no mention of use cases, exclusions, or comparisons. The only hint is the 'days' parameter in the schema, but it is not referenced in the description.

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 provided, the description must fully disclose behavioral traits. It only mentions 'recent' (近期), hinting at a time scope, but fails to describe return format, error handling, language of news, or any side effects. This is a significant gap for a tool that fetches external data.

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 directly states the tool's function without any filler or redundancy. It earns its place by being concise and efficient, typical of a well-sized tool description.

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

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

The tool is simple with only two parameters, but the absence of an output schema and annotations means the description should cover more ground. It lacks a clear distinction from stock_news_global and does not describe what the response looks like, making it minimally complete for a news-fetching 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 description coverage is 100% for both parameters, so the baseline is 3. The description repeats the symbol semantics already in the schema ('股票代码或加密货币符号') and adds no additional meaning for the limit parameter. It does not compensate beyond the schema's existing field 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 uses the specific verb '获取' (get) and identifies the resource as '近期相关新闻' (recent related news) for a given stock code or cryptocurrency symbol. This clearly distinguishes it from sibling tools like stock_prices, stock_info, and stock_news_global, making the tool's purpose unambiguous.

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 fetching news about a specific symbol but does not explicitly state when to use this tool versus alternatives such as stock_news_global or search. There is no mention of exclusions or preferred contexts, leaving the when-to-use guidance implicit at best.

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 provided, the description carries the full burden, but it only states that the tool retrieves the latest global financial news. It does not disclose response format, data source, frequency, or any limitations, leaving the agent with minimal behavioral insight.

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 concise sentence that front-loads the action and object. There is no filler, repetition, or unnecessary detail.

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 zero-parameter tool, the description covers the basic function, but it omits expected return value format, ordering, or how it differs from the sibling stock_news tool. It is minimally adequate but lacks enough context for confident invocation in ambiguous situations.

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 schema has zero parameters, so there is nothing to document. The description appropriately adds the semantic of 'latest' and 'global financial news,' which is all that is needed given the parameterless interface.

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 uses a specific verb ('获取' = fetch) and a clear resource ('全球财经快讯' = global financial news), making the purpose obvious. It distinguishes from sibling stock_news by emphasizing 'global' scope, though it does not explicitly compare to alternatives.

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 given on when to use this tool versus sibling tools like stock_news or stock_prices. There are no context cues, exclusions, or alternative recommendations, so the description offers no decision support.

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 the full burden. It discloses only one limitation (no crypto support) and does not describe return format, error behavior, or required permissions. This is minimal behavioral disclosure.

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 is front-loaded with the core purpose. It contains no waste and is easily parsed.

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 output schema and annotations, the description should clarify return structure. It only mentions historical prices and technical indicators, but not the format or specifics. Still, for a straightforward price-fetching tool, this is minimally adequate.

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 schema provides 100% coverage of all 4 parameters with clear descriptions. The tool description adds little beyond referencing symbol and market, so it does not enhance parameter semantics.

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 retrieves stock historical prices and technical indicators based on symbol and market. It also notes crypto is not supported, which distinguishes it from cryptocurrency tools. However, it does not differentiate from sibling stock indicator tools, so it lacks sibling differentiation.

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 the primary use case (fetching historical prices/indicators) and explicitly excludes cryptocurrency. It does not mention alternatives or when to use other sibling tools like stock_indicators_a. This leaves some ambiguity about tool selection.

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 must disclose behavioral traits. It only states that it retrieves data, but does not mention the trading-day constraint, return format, or any limitations. The schema contains details about parameter values, but the description itself adds no behavioral context.

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 concise sentence that states the purpose without redundancy. It earns its place but lacks some useful context, so it is efficient but not exceptionally structured.

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

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

The tool has only two parameters and no output schema, but the description is minimal. It does not explain the return data, the ranking aspect implied by the tool name, or the availability of concept/regional categories, leaving the description incomplete for a full understanding.

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 schema already provides full descriptions for both parameters (cate and days) with allowed values, achieving 100% coverage. The description does not add any additional parameter information, so a 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 uses the specific verb '获取' and identifies the resource as '中国A股市场的行业资金流向数据', making the core function clear. However, it does not distinguish this tool from sibling tools that also fetch stock-related data, and the description mentions only '行业' (industry) while the schema supports '概念' and '地域' as well.

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?

There is no guidance on when to use this tool vs alternatives, no exclusions, and no mention of related tools. The description only states the function without any contextual usage direction.

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 but only restates the basic function. It does not disclose that the '所有' (all) result is subject to the limit parameter (30-100), nor does it mention data source, return structure, or other behavioral characteristics.

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, concise sentence that immediately states the tool's purpose without any redundant words or filler. Front-loaded and efficient.

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 is minimal for a tool with no output schema and a close sibling (stock_zt_pool_strong_em). It does not clarify what data is returned (e.g., stock codes, prices), does not differentiate from the sibling, and the claim of '所有' (all) is contradicted by the limit parameter's cap on returned count.

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 descriptions cover 100% of the parameters (date and limit), providing baseline value. The description itself adds no extra parameter context, but it does not need to since the schema is sufficient.

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 uses a specific verb (获取) and resource (中国A股市场涨停股票) with market scope (上证/深证), clearly stating what the tool does. It does not explicitly distinguish from the sibling stock_zt_pool_strong_em, but the 'all' qualifier and market scope provide moderate differentiation.

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. There is no mention of scenarios, exclusions, or comparison to the similar stock_zt_pool_strong_em tool, leaving the user to infer usage.

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 provided, the description carries the entire burden of behavioral disclosure. It only states that it fetches data, omitting any details about default date behavior, return format, meaning of 'strong', or limitations. This is a complete lack of transparency beyond the basic function.

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, direct sentence with no redundant wording. It is efficiently front-loaded and concise, earning its place by clearly stating the tool's purpose without any fluff.

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

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

For a tool with no output schema and no annotations, the description is minimal and incomplete. It lacks usage guidelines, parameter behavior context, and differentiation from similar stock pool tools, making it insufficient for an agent to fully understand when and how to invoke it effectively.

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 schema already describes both parameters (date and limit) with formats and defaults, achieving 100% coverage. The description adds no extra meaning to the parameters, so the baseline of 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 it retrieves strong stock pool data for the Chinese A-share market (SSE/SZSE), using a specific verb-resource combination. However, it does not explicitly differentiate from sibling tools such as stock_zt_pool_em, which may also relate to stock pools, so it lacks full distinction.

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?

There is no guidance on when to use this tool versus alternatives like stock_zt_pool_em or stock_lhb_ggtj_sina. The description gives no use cases, prerequisites, or exclusions, leaving the agent without decision support for tool selection.

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 provided, the description carries the full burden of behavioral disclosure. It does not describe side effects, dependencies on prior tool calls, error handling, or what the output looks like. It only states that it bases advice on AI analysis results, leaving most behavioral traits unexplained.

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 is concise and front-loaded. It avoids unnecessary words and clearly conveys the core idea without bloat.

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 absence of annotations and output schema, and the moderate complexity of the tool, the description is too sparse. It fails to explain that this tool is likely the final step in a multi-tool workflow, how it integrates with sibling data-gathering tools, or what the user can expect as a result. This leaves significant gaps for the 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 input schema provides complete descriptions for all 4 parameters (100% coverage), so the baseline is 3. The description adds minimal context by indicating the 'reason' should stem from AI analysis, but it does not provide meaningful detail beyond what the schema already offers.

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's purpose: to give specific investment advice based on AI analysis of data from other tools. It uses a specific verb ('give') and resource ('investment advice'), which distinguishes it from the mostly data-retrieval sibling tools. However, it doesn't explicitly differentiate from similarly named AI report tools like binance_ai_report.

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 should be used after other tools have gathered data, but it does not explicitly state when to use it versus alternatives, nor does it mention any exclusions or prerequisites. There is no guidance on when not to use it.

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