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

With no annotations provided, the description carries full responsibility for behavioral disclosure. It only says '获取报告' (get report) without mentioning any side effects, output format, data source specifics, or limitations. This is minimal for a tool that returns an analysis report.

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 the second sentence ('此工具对分析加密货币非常有用，推荐使用') is subjective filler that doesn't add functional information. The first sentence earns its place, but the second is unnecessary.

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 output schema and no annotations, yet the description does not explain what the report contains or how the agent should use it. The single parameter is documented, but overall the description is insufficient to fully inform an agent about expected return values or 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 schema description for 'symbol' is clear ('加密货币币种，格式: BTC 或 ETH'), providing 100% coverage. The tool description itself adds no additional parameter meaning, so the baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool fetches Binance's AI analysis report on cryptocurrencies, using the specific verb '获取' (get/fetch) and identifying the resource and scope. It is distinct from sibling crypto tools like crypto_prices or crypto_sentiment_metrics, though it doesn't explicitly compare to them.

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 only usage guidance is '此工具对分析加密货币非常有用，推荐使用' (this tool is very useful for analyzing cryptocurrencies, recommended to use), which is a vague recommendation rather than specific when-to-use or when-not-to-use guidance. It does not mention alternatives or exclusions.

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 conveys a safe read-only operation and indicates the output includes system time and A-share trading day information, but it does not specify timezone, return format, or the exact content of '交易日信息'. Basic transparency is present, but richer details are missing.

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: it first states the main function, then gives a practical usage recommendation. There is no filler or redundant content, 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?

The tool is simple with zero parameters and no output schema, so the description does most of the work. It clearly explains the purpose and implies the output can be used as a date parameter for other tools, though it does not explicitly define the return structure. Given the low complexity, this is adequately 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 tool has zero parameters, and the input schema is an empty object, so the description need not document parameter syntax. The baseline for zero-parameter tools is 4, and the description adds a useful hint that its output can be used to fill date parameters in other tools.

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). This is a specific verb+resource combination that distinguishes it from sibling market-data 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 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 usage context. It does not list exclusions or alternatives, but the recommendation is strong enough for a utility 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?

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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds valuable context: '不支持加密货币' (cryptocurrencies not supported) and '比较耗时' (relatively time-consuming). However, it does not disclose the return format or how results are presented (e.g., single code vs. list), which is important for a search tool without an output schema. No annotation contradiction exists.

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 exceptionally concise: two sentences that front-load the core purpose and immediately follow with actionable usage guidance. Every clause adds value—the first sentence states function and limitation, the second explains when to avoid the tool. No wasted words or 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?

Given the tool's simplicity (2 params, 1 required, no output schema), the description covers the key aspects: purpose, scope (no crypto), performance, and selection guidance. The only notable omission is an explicit statement of what the tool returns (e.g., a list of matching stock codes), but this is strongly implied by the title and purpose. Overall, near-complete for this low-complexity 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?

The input schema already covers both parameters with 100% coverage: 'keyword' is described as requiring company/stock names or codes, and 'market' is fully documented with allowed values. The tool description restates '股票名称、公司名称等关键词', duplicating the keyword schema. It adds no new parameter-specific meaning, so the baseline score 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 the tool's function: '根据股票名称、公司名称等关键词查找股票代码' (find stock codes based on keywords such as stock/company names). It uses the specific verb '查找' (find) and names the resource '股票代码', distinguishing it from sibling tools that operate on already-known codes. The explicit exclusion of cryptocurrencies further clarifies its scope relative to crypto-related siblings.

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 explicit usage guidance: '该工具比较耗时，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' (This tool is time-consuming; when you know the code or user has specified one, directly use other tools with the code). This tells the agent when to avoid the tool and to prefer alternatives, making the decision clear, even though it doesn't name specific sibling tools.

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 for behavioral disclosure. It only states the basic function and market scope, without mentioning data source, update frequency, potential limitations, or any other behavioral characteristics. The description adds minimal context beyond the title.

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 essential information: what the tool does and its market scope. There is no redundant or filler 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?

With only one parameter and no output schema, the description is fairly simple. However, it does not clarify what 'key indicators' are included, which could be important for an agent deciding whether to use this tool. The absence of an output schema increases the need for such detail, making the description only 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 description coverage is 100% because the 'symbol' parameter has the description '股票代码' (stock code). The tool description adds no additional parameter semantics 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 uses a specific verb '获取' (retrieve) and a clear resource '股票财务报告关键指标' (key financial report indicators). It explicitly scopes to China's A-share market (Shanghai, Shenzhen), which distinguishes it from sibling tools like stock_indicators_hk and stock_indicators_us. The purpose is 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 A-share stocks by specifying the market, but it does not explicitly state when to use this tool versus alternatives or mention any exclusions. No alternative tools are referenced. Usage is inferred rather than articulated.

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 does not mention return format, potential errors, data source, or any side effects. This is a minimal disclosure that only states the basic purpose, which is insufficient for a tool with no annotation support.

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 redundant content. It is front-loaded with the primary verb and resource, making it easy for an agent to quickly understand 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 with only one parameter and no output schema, but the description is brief and does not specify which key indicators are included or what the response structure looks like. While adequate for a simple fetch, it leaves some gaps that could hinder an agent's ability to fully anticipate the tool's 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?

The schema description coverage is 100% (the 'symbol' parameter has a description '股票代码'), and the tool description adds no additional semantic meaning to the parameter. Per the baseline rule, a score of 3 is appropriate when the schema already covers parameter 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 that the tool retrieves key financial report indicators for Hong Kong stocks, using a specific verb ('获取') and a specific resource ('港股市场的股票财务报告关键指标'). It distinguishes itself from sibling tools like stock_indicators_a and stock_indicators_us by explicitly mentioning the HK market, though it doesn't explicitly contrast with them.

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 usage context is implied: the tool is for HK stocks, and given sibling tool names (stock_indicators_a, stock_indicators_us), an agent can infer when to use this tool. However, there is no explicit guidance on when to use it versus alternatives or any exclusions, leaving the differentiation largely to the tool name.

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 carry the full burden of behavioral disclosure. It implies a read-only 'get' operation but lacks details on permissions, data sources, return format, or limitations. The description does not contradict annotations (none exist), but it 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, concise sentence that directly conveys the tool's purpose. It wastes no words and is front-loaded with the essential 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?

The tool is simple with one parameter and no output schema, but the description is vague about what 'key indicators' are returned. It does not explain the output format or provide additional context that would help a user understand the tool's capabilities fully. It is minimally viable but lacks depth.

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% (the only parameter 'symbol' is described as '股票代码'). The description does not add extra meaning beyond the schema, but the baseline is 3 due to 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 fetches key indicators from US stock financial reports, using a specific verb (获取) and resource (美股市场的股票财务报告关键指标). It distinguishes itself from sibling tools like stock_indicators_a and stock_indicators_hk by explicitly scoping to US stocks.

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 clearly indicates the tool is for US stock market financial indicators, providing clear context for when to use it. However, it does not explicitly mention alternatives or exclusions, such as 'for A-shares use stock_indicators_a'.

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, the description carries the full burden of behavioral disclosure. It only states the core purpose and does not mention read-only nature, data source (Sina, only in tool name), rate limits, pagination, or return format. The schema covers the '仅支持' constraint for days, but the description 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, grammatically correct sentence with no filler. It front-loads the action and resource, being appropriately concise for a simple lookup tool.

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

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

While the tool is simple (two fully-documented optional parameters), the absence of an output schema means the description should explain return values. It does not, leaving a gap. However, the core purpose is clear enough for basic usage, making it 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 description coverage is 100%, with both days and limit having descriptions. The tool description adds no parameter-specific meaning beyond the schema, so the baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb '获取' (get) and the specific resource: 龙虎榜个股上榜统计数据 (Dragon-Tiger List individual stock statistics) for A-shares in Shanghai and Shenzhen. This differentiates from sibling tools like stock_zt_pool_em (limit-up pool) and northbound_funds.

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 alternatives such as stock_zt_pool_em or stock_sector_fund_flow_rank. There are no exclusions, prerequisites, or recommended scenarios described.

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?

No annotations are present, so the description carries the full burden. It does not disclose whether the operation is read-only, any rate limits, response format, or other behavior. The description only restates the basic action without adding beyond-obvious 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?

The description is a single, short sentence that directly states the tool's purpose. It is front-loaded and contains no unnecessary words or filler.

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

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

For a no-parameter, low-complexity tool, the description is minimally viable: the agent can infer that calling it returns current global financial news. However, there is no output schema and the description does not specify the returned data structure, fields, or source, leaving the agent uncertain about the response form.

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 has zero parameters, so the description cannot add parameter-level meaning. The input schema is already complete with no properties, and the baseline for zero parameters is 4.

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' - get) and resource ('全球财经快讯' - global financial news), clarifying that this tool returns the latest global financial news. It does not explicitly contrast with sibling tool stock_news, though the 'global' qualifier partially differentiates it.

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 about when to choose this tool over alternatives like stock_news or market_prices. There is no mention of scenarios, exclusions, or recommended use cases, leaving the agent to infer.

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 carries the full transparency burden. It discloses a read operation ('获取') but does not describe output structure, data source, or any side effects. Additionally, the description says '行业' (industry) while the schema allows multiple category types, which is a transparency gap.

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 communicates the core function with no unnecessary words. It is appropriately sized and easy to understand.

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 low complexity (2 parameters, no output schema, no annotations), the description provides the essential purpose but falls short by not mentioning the ranking behavior (from the name) or the broader category support in the schema. It is minimally viable but lacks full contextual richness.

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%, with both parameters (cate and days) having detailed descriptions of allowed values. The tool description does not add any extra parameter semantics beyond what the schema already provides, so it stays at the baseline.

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 a specific action ('获取' / get) and target ('中国A股市场的行业资金流向数据' / China A-share market industry capital flow data), which aligns with the tool's name and title. However, it does not mention the 'rank' aspect or distinguish this tool from sibling sector-related tools like sector_rotation or sector_valuation. It also narrows the scope to '行业' (industry) while the schema supports '概念' and '地域' categories.

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. The schema's parameter description for 'days' includes a caution about trading days, but the main description offers no contextual decision support or comparisons with related tools.

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 claims to return 'all' limit-up stocks, but the limit parameter (default 50, range 30-100) contradicts this, indicating a cap on results. The description does not disclose this limitation, nor any other behavioral details like rate limits or return format.

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?

A single, concise sentence that immediately states the tool's purpose with no filler words. It is well structured and front-loaded.

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 an output schema and annotations, the description should explain what data is returned (e.g., stock codes, names, statistics) and clarify the limit cap. It fails to do so, making the description incomplete for a tool that returns a data pool.

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?

Both parameters are already fully described in the schema (date format and limit range/default), giving 100% schema coverage. The description adds no additional meaning 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 uses the specific verb '获取' (get) and identifies the resource as all limit-up stocks in China's A-share market (Shanghai, Shenzhen), clearly stating the tool's function. However, it does not distinguish itself from the sibling tool stock_zt_pool_strong_em, which likely serves a similar purpose.

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. The description does not mention any conditions, exclusions, or prerequisites, and the optional date/limit parameters are only documented in the schema, not in 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?

No annotations are provided, so the description carries full responsibility for behavioral disclosure. It only states 'get data', which implies a read operation, but it does not disclose any return format, additional behavior, or limitations. It offers minimal insight beyond what the tool name already suggests, failing to provide meaningful 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 tool's purpose without any fluff or redundancy. It is appropriately sized for a simple data retrieval tool and front-loaded with the key action and target. Every word earns its place.

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

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

While the tool has only two parameters and no output schema, the description is notably incomplete. It does not define what constitutes a 'strong stock pool', nor does it explain how this tool relates to the similarly named stock_zt_pool_em. Without this context, an agent cannot judge when to use this tool or what to expect from the results, leaving a significant gap despite the tool's seeming simplicity.

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 tool description itself adds no information about parameters; it merely names the resource. The schema already explains the date format, default behavior, and limit range, so the description does not need to compensate. This is an acceptable passing score for 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 it retrieves Chinese A-share market strong stock pool data with a specific verb ('获取') and resource ('强势股池数据'). It is clear and specific, but it does not explicitly distinguish itself from the sibling tool stock_zt_pool_em, which appears to be a related but different pool (regular limit-up pool vs strong pool). Thus it meets the 'clear but no sibling differentiation' criterion.

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. There is no mention of when to prefer 'strong stock pool' over the regular limit-up pool or other stock data tools. The name implies distinction, but the description itself gives no usage context or exclusions.

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 disclosing behavior. It does not state whether the tool is a read-only suggestion, a write/record operation, or how it communicates results. No side effects, permissions, or return behavior are mentioned, leaving the tool's actual execution opaque.

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 communicates its core purpose efficiently. It is front-loaded and avoids verbosity, though it omits important contextual details that would make it more useful.

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 and no annotations, the description should explain what the tool returns and any behavioral nuances. It does not describe return values, side effects, or how the advice is delivered. For a 4-parameter tool, this is notably incomplete.

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%, and the schema already explains each parameter (symbol, action, score, reason) adequately. The description adds no additional meaning to the parameters, but the baseline of 3 applies 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 states the tool 'gives specific investment advice based on AI analysis results of data from other tools,' which is a clear verb+resource. However, it does not distinguish itself from sibling tools like composite_stock_diagnostic or binance_ai_report that also generate advice, and the input schema (containing action, score, reason) suggests the tool may present pre-computed advice rather than generate it, creating 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?

There is no guidance on when to use this tool versus alternatives. It merely describes what it does without mentioning prerequisites, exclusions, or when not to use it. The phrase 'based on AI analysis of other tools' hints at a sequence, but no explicit usage context is provided.

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