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

No annotations are provided, so the description must fully disclose behavior. It mentions output metrics but omits critical details like data frequency, transaction costs, slippage, or performance limits. The description is insufficient for understanding side effects or constraints.

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 key details. No wasted words; every element 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 the absence of output schema and annotations, the description covers purpose, supported strategies, and output metrics. However, it lacks completeness on data specifics, assumptions, and output structure, leaving gaps for a backtesting 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?

Input schema covers 100% of parameters with clear descriptions. The tool description adds context for strategy types and default values, but does not significantly enhance parameter meaning beyond schema. 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 performs backtesting on stocks with specific strategies (MA cross, MACD, KDJ, etc.) and returns key metrics (returns, max drawdown, win rate). It is distinct from sibling tools that focus on data retrieval or 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 lists supported strategies but provides no guidance on when to use this tool versus alternatives, nor does it mention prerequisites, data requirements, or limitations. No explicit when/when-not advice.

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

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

With no annotations, the description carries full burden. It only discloses automatic retry, omitting details like rate limits, data freshness, or whether it's a read-only operation.

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

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

Two short sentences cover purpose and a notable feature (retry). Slight redundancy in mentioning 'analyzing cryptocurrencies' twice, but overall 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?

For a tool with one parameter and no output schema, the description should explain what the report contains or how to interpret it. Lacks completeness.

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%; the parameter 'symbol' is described adequately in the schema. The description adds no additional meaning beyond the schema.

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

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

The description clearly states it retrieves Binance's AI analysis report on cryptocurrencies, with a specific verb and resource. However, it does not differentiate from sibling tools like 'trading_signals' that might also analyze cryptocurrencies.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives. It mentions automatic retry but no context about prerequisites or typical use cases.

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

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

无注解，描述仅简单说明查看状态，未明确提及只读性、权限或副作用，信息不足。

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?

一句话描述，无冗余，高度简洁。

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?

工具简单无参数，描述基本覆盖功能，但未说明返回内容或状态细节，可进一步完善。

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?

输入模式无参数，描述无需补充参数信息，符合基线4分。

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?

描述明确说明'查看多数据源的状态和熔断器信息'，动词和资源清晰，与众多股票交易工具区分明显。

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?

未提供任何使用时机或替代工具的指导，面对众多兄弟工具缺乏上下文。

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?

The description mentions auto-retry ('支持自动重试'), but lacks other behavioral details like rate limits, error handling, or data freshness. With no annotations, more transparency would be expected.

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

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

Two concise sentences, front-loaded with purpose and useful note on auto-retry. No wasted words.

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

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

No output schema, and the description does not explain the return format or structure. For a simple tool with two optional params, it is adequate but could be improved by describing the response.

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

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

Schema coverage is 100%, with clear parameter descriptions. The description adds no additional semantics beyond the schema, meeting 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 it retrieves the cumulative loan ratio between borrowed quote and base currencies on OKX, which is distinct from sibling tools like okx_prices or 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 on when to use this tool versus alternatives like okx_prices or trading_signals, or any prerequisites.

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

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

No annotations are provided, so the description carries full burden. It mentions 'supports automatic retry', which is a behavioral trait. However, it does not disclose read-only nature, rate limits, error behavior, or other important details. The auto-retry info adds some value, but more context is needed.

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 very concise: one sentence plus 'supports automatic retry'. It is front-loaded with the main action. While no words are wasted, it could benefit from a more structured format listing typical use cases or output details.

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, so the description should explain return values. It mentions data includes price, volume, and technical indicators, but not the format or structure. With three simple parameters and no required fields, the description is mostly adequate but lacks explicit output details.

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 with default values and constraints, so the schema already explains each parameter well. The description adds that the data 'includes price, trading volume and technical indicators', which provides context beyond parameter definitions but does not add substantial semantic detail.

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 gets OKX cryptocurrency historical K-line data, including price, volume, and technical indicators. The verb '获取' (get) and resource 'OKX加密货币的历史K线数据' are specific. It distinguishes from siblings like stock_prices and other OKX tools (okx_loan_ratios, okx_taker_volume) by focusing on crypto K-line data.

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 OKX crypto data but does not explicitly state when to use this tool versus siblings like stock_prices or altcoins. No when-not or alternative tools are mentioned, leaving the agent to infer from context.

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

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

No annotations provided, so the description carries full burden. It mentions 'supports automatic retry' which is useful, but fails to disclose other behaviors like error handling, data freshness, or required permissions.

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

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

Two sentences clearly state purpose and retry behavior. Front-loaded with the main action, no wasted words.

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

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

No output schema, no annotations. The description omits return format, error conditions, and data interpretation. For a 3-parameter tool, more context is needed for completeness.

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?

All three parameters have schema descriptions covering format and values (e.g., '5m/1H/1D' with case sensitivity). The tool description adds 'supports automatic retry' but no extra parameter context. With 100% schema coverage, baseline is 3.

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

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

Description clearly states the tool retrieves OKX cryptocurrency taker volume (active buy/sell). It distinguishes itself from sibling tools like okx_prices and okx_loan_ratios by specifying 'taker volume'. However, the description is in Chinese, which may affect clarity for English-only agents.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives. No mention of prerequisites, scenarios, or when not to use it. Sibling tools like okx_prices or backtest_strategy are not referenced.

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 burden. It does not disclose whether this is a read-only operation, if data is cached, or any side effects. The description only lists metrics, not behavioral traits.

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

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

Single sentence that is efficient and front-loaded with key purpose. No redundant words.

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

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

With no output schema and no annotations, the description is too bare. It does not explain return format, interpretation, or any constraints like minimum days or data source. For a risk analysis tool, more context is needed.

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

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

Input schema covers 100% of parameters with descriptions. The description adds no additional meaning beyond what the schema provides, so 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 tool analyzes portfolio risk metrics including specific indicators like volatility, max drawdown, correlation matrix, and Sharpe ratio. This is distinct from sibling tools which are mostly single-stock or market-level analysis 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?

No guidance on when to use this tool vs alternatives, prerequisites, or context. Users are left to infer that it's for portfolio-level risk analysis, but no explicit conditions or exclusions are mentioned.

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

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

With no annotations 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 burden. It only mentions automatic failover but lacks disclosure of other behaviors like rate limits, data freshness, error handling, or maximum batch size, which is insufficient for a data-fetching 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 two sentences, front-loaded with the core purpose, and contains no waste.

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 and no annotations, so the description is the only source. It notes automatic failover but omits critical details like maximum symbols, return format, and error handling, making it adequate but 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 coverage is 100%, and the description adds no extra meaning beyond what the schema already provides for symbols and limit. 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 '批量获取多只A股实时行情数据' (batch get real-time market data for multiple A-shares), specifying the verb, resource, and scope, and distinguishes from single-stock siblings like stock_realtime.

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 mentions '支持多数据源自动故障转移' (supports automatic failover), implying reliability, but does not explicitly state when to use this tool vs alternatives, nor provides when-not-to-use guidance.

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

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

With no annotations provided, the description bears full responsibility for behavioral disclosure. It mentions the data fields returned (price, volume, premium rate) but provides no information on authentication, rate limits, pagination, or potential side effects. For a read operation, this minimal transparency is insufficient.

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 (two sentences) and front-loaded with the core purpose. Every sentence serves a clear role: stating the data provided and its significance. No redundant or unnecessary information, making it efficient for an AI agent to parse.

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 two optional parameters and no output schema, the description sufficiently covers the main purpose and key data points. However, it lacks details on output format, real-time vs. historical nature, and the effect of the 'limit' parameter. Still, it is mostly complete given the tool's 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?

The input schema has 100% coverage: both 'symbol' and 'limit' are described with defaults and explanations. The description adds context about the data contents (e.g., premium rate) but does not elaborate on parameter behavior (e.g., how symbol filtering works or how limit interacts with output). Given high schema coverage, the description meets the baseline without adding substantial param-specific 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 clearly states the tool retrieves A-share block trade data including price, volume, and premium rate, and explains its significance for institutional trading. The verb '获取' (get) and resource are specific, and it differentiates itself among sibling tools by focusing on a unique data type (大宗交易).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for obtaining block trade data but does not explicitly state when to use this tool versus alternatives like stock_realtime or stock_fund_flow. There is no guidance on prerequisites, context, or exclusions, leaving the agent to infer usage from the tool's name and description alone.

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 mentions automatic failover across multiple data sources, which is a useful behavioral trait. However, with no annotations provided, the description does not disclose whether the tool is read-only, requires authentication, or has any rate limits, leaving significant behavioral 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 consists of two concise, front-loaded sentences. The first sentence states the core purpose, and the second adds a key behavioral note. No unnecessary words or 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 list tool with three parameters, the description covers purpose and a notable behavior (failover). However, with no output schema, the description does not indicate return format, error behavior, or sample output, leaving some incompleteness.

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 all parameters with clear descriptions and examples, achieving 100% coverage. The description adds no additional semantic value beyond what the schema provides, meeting the baseline expectation.

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 constituent stock lists for industry or concept sectors with specific examples of board names, making the purpose straightforward. However, it does not explicitly differentiate from sibling tools like stock_sector_spot or stock_sector_fund_flow_rank, which could cause confusion.

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 are no scenarios, prerequisites, or exclusions mentioned, leaving the agent without context 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, the description should have disclosed behavioral traits like read-only nature, market restrictions, or error handling. It only lists output fields, omitting potential behavior for non-A-shares and other details.

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 efficiently conveys the tool's purpose and output fields without any extraneous content.

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

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

Given the complexity (1 parameter, no output schema), the description is incomplete. It fails to specify output structure, market scope limitations, or how the data is returned, leaving important gaps.

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 coverage for the 'symbol' parameter with a clear description. The tool description adds no additional semantics beyond the schema, meeting the baseline for 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 it retrieves chip distribution data for A-shares and lists key fields. However, it slightly contradicts the input schema which allows US and HK symbols, causing 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. There is no mention of use cases, prerequisites, or when not to use it, leaving the agent without 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 must disclose behaviors. It mentions data types but omits details like data frequency, historical range, whether only A-shares, rate limits, or error handling. The lack of behavioral context limits agent decision-making.

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

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

Two concise sentences with no wasted words. The purpose is front-loaded, and the description efficiently communicates core functionality.

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

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

With no output schema, the description should outline return fields. It mentions basic data types but lacks a structured list of fields (e.g., dividend date, amount, type). For a simple 2-parameter tool, this is adequate but not 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?

Schema description coverage is 100%, so baseline is 3. The description does not add meaning beyond the schema for parameters (symbol, limit) but enriches understanding of the output by listing data types (派息、送股、转增). This is marginal value, so score remains 3.

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

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

The description clearly states it retrieves historical dividend data for A-share stocks, listing specific types (派息、送股、转增) and a purpose (分析股息率和分红政策). It distinguishes from other stock tools like stock_prices or stock_financials by focusing solely on dividends.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies use for dividend analysis ('用于分析股息率和分红政策') but does not explicitly state when to use this tool versus alternatives like stock_financials or stock_info. No direct comparison or conditions for use/avoidance are provided.

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, and the description lacks behavioral details such as whether the tool is read-only, rate limits, data freshness, or any side effects. It only states what it does without 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 concise sentence, front-loading the key purpose without any redundant words. It earns its place efficiently.

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 (no output schema, simple parameters), the description is partially complete. It misses behavioral context and return format, but for a calendar tool, it is minimally viable.

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

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

The input schema has 100% description coverage for its two parameters, providing adequate documentation. The description adds no extra meaning beyond the schema, 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 clearly states the tool retrieves the A-stock earnings disclosure schedule and lists upcoming companies, with a specific verb and resource. It differentiates from the sibling 'stock_earnings_us' by specifying 'A股'.

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 mentions '用于跟踪财报季' (used for tracking earnings season), providing context. However, it does not explicitly exclude cases or mention alternatives like 'stock_earnings_us' for US stocks.

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 partially discloses behavioral traits: it mentions data source fallback (Alpha Vantage with API key to yfinance free). However, it does not discuss rate limits, error handling, data freshness, or whether the data is for a specific time range. The disclosure is helpful but incomplete.

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

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

Two concise sentences, front-loaded with purpose, followed by technical detail. No unnecessary words; every sentence adds value.

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

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

Given the tool's simplicity (one parameter, no output schema), the description covers purpose and data source but fails to explain the output format or expected return values. An agent would not know what data fields to expect, which is a gap in completeness.

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

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

Schema coverage is 100% with a clear description of the 'symbol' parameter. The description adds context about what data is fetched and data source behavior, but does not provide additional meaning for the parameter beyond stating it's a US stock symbol. 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 retrieves historical earnings data and analyst expectations for US stocks, with specific verb '获取' and resource '美股历史盈利数据和分析师预期'. It differentiates from siblings by mentioning historical earnings and analyst expectations, though it doesn't explicitly contrast with similar tools like stock_earnings_calendar.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives like stock_earnings_calendar or stock_financials_us. The description lacks context on prerequisites, such as requiring an API key for Alpha Vantage, and does not specify typical use cases or limitations.

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 burden. It states that the tool gets financial indicators across dimensions but does not disclose whether it is read-only, any data limitations, authentication needs, rate limits, or response behavior. The description is too brief for safe agent invocation.

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

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

The description is a single sentence that is front-loaded with the core purpose. Every word is relevant, and there is no unnecessary 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 absence of an output schema and annotations, the description should explain what data is returned (e.g., specific metrics, structure). It only mentions '多维度分析' vaguely. This is insufficient for a tool that likely returns complex financial data, especially with many sibling tools.

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

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

Schema coverage is 100% with a detailed description for the 'symbol' parameter including format examples. The description adds no further 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' and resource 'A股个股详细财务指标', clearly indicating it retrieves detailed financial indicators for A-share stocks. It mentions multiple dimensions (盈利, 偿债, 运营) and distinguishes from siblings like stock_financials_us (US-focused) and stock_valuation_compare (valuation-focused).

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. With numerous related siblings (e.g., stock_indicators, stock_financials_us), explicit context for selection is missing. The agent must infer usage from the tool's name and title.

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 cover behavioral traits. It explains the multi-source fallback behavior and that yfinance is free, but it omits details like failure modes, rate limits, or whether the tool is read-only. The fallback order is useful but insufficient 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 two sentences long, front-loaded with the core purpose and supported by data source details. Every word adds value; no unnecessary 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 three parameters, full schema coverage, and no output schema, the description covers the data source strategy and report types. It lacks details on return format or error handling, but given the tool's complexity, it provides adequate context for an agent to use it correctly.

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

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

The input schema has 100% description coverage for all three parameters. The description adds context about data sources and the types of reports (balance_sheet, etc.), but this is largely redundant with the schema. No additional parameter-level details are provided beyond what the schema already conveys.

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 US stock financial statements (balance sheet, income statement, cash flow). It distinguishes from siblings by specifying the data source fallback (Alpha Vantage then yfinance), which is unique among related 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 provides guidance on when to use each data source: Alpha Vantage requires an API key, while yfinance is free. This helps the agent prioritize sources. However, it does not explicitly contrast with sibling tools like stock_financial_compare or stock_earnings_us, leaving some ambiguity.

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 adds value by disclosing multi-data source automatic failover, but lacks details on rate limits, update frequency, or output 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?

Two sentences, front-loaded with the main purpose, followed by a key feature, no wasted words.

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

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

The description covers the core functionality and a notable feature (failover) for a simple one-parameter tool without output schema; lacks return format hints but is largely 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 provides a comprehensive description of the symbol parameter with examples and format, and the tool's description adds no additional parameter info, meeting the baseline for 100% 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 it retrieves A-share individual stock capital flow data, specifying order sizes (主力, 超大单, etc.) and inflow/outflow, with a specific verb and resource.

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 capital flow but provides no explicit guidance on when to use this tool vs siblings like stock_sector_fund_flow_rank or stock_chip, nor prerequisites 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 only states the tool retrieves change data and gives an interpretation hint, but fails to describe the output format (e.g., time series of counts), data range, or any constraints.

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

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

The description is two concise sentences that front-load the core action and add a brief interpretive note. Every word contributes value without redundancy.

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

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

For a tool with one parameter and no output schema, the description is incomplete. It omits what the returned data structure looks like (e.g., fields, dates), error conditions, or any additional context needed for an AI agent to correctly interpret and use the output.

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 fully describes the 'symbol' parameter with a clear example. The description does not add or repeat parameter information, which is acceptable given 100% 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 change data of A-share shareholder count, using the verb '获取' and specifying the resource '股东户数变化数据'. It also distinguishes itself from sibling tools like stock_prices by focusing on shareholder concentration, 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 implies usage for analyzing chip concentration by interpreting a decrease in shareholder numbers, but it does not explicitly state when to use this tool versus alternatives, nor does it provide exclusions or prerequisites.

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

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

Without annotations, the description only indicates '获取' (get) implying read-only, but it doesn't describe data freshness, market coverage limitations, or whether it returns a single value or a set. Behavioral traits are barely 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?

Single sentence conveying purpose and market support. No redundant information, front-loaded, and highly 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 lacks details about the output: what constitutes 'key indicators'? No output schema exists, so the agent has no idea what data shape to expect. Missing return value explanation leaves the tool incomplete for autonomous use.

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

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

Schema coverage is 100%, and both parameters have descriptions. The tool's description adds the market support context ('支持A股、港股、美股市场'), which aligns with the market param. No additional semantic depth beyond schema.

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

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

The description clearly states it retrieves key financial indicators from reports and supports three markets. However, it does not specify what kinds of indicators (e.g., ratios, margins) which would make it more distinctive.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool vs. siblings like stock_financial_compare or stock_financials_us. The agent receives no context to differentiate it from similar financial 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 burden. It only states the tool 'gets data' without disclosing behavioral traits such as read-only nature, rate limits, return format, or any side effects. This is insufficient 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?

A single, front-loaded sentence that efficiently communicates the tool's function and purpose. No unnecessary words.

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

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

The tool has no output schema, so the description should explain what the return data looks like (e.g., which industries, PE values). It does not. The context signals indicate a simple tool, but the lack of return format information leaves the agent uncertain about how to use the output.

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% (one parameter with description). The description adds no additional meaning beyond what the schema already provides, so 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 that the tool retrieves A-share industry PE comparison data and provides the purpose (industry valuation comparison and rotation). It distinguishes itself from stock-level tools but does not explicitly differentiate from sibling tools like stock_market_pe_percentile or stock_valuation_compare.

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 industry analysis but provides no explicit guidance on when to use this tool versus alternatives, no when-not to use, and no mention of prerequisites or 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 must disclose behavioral traits. It only states a limitation (no crypto support) but omits details like read-only nature, required permissions, rate limits, or side effects. The description is insufficient for an agent to understand operational behavior.

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

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

Description is a single sentence that is front-loaded and contains no unnecessary words. Every part of the description adds value.

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

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

Given the tool has no output schema, the description does not explain what 'basic stock info' includes, leaving the expected output ambiguous. However, for a simple tool with only two parameters, the description 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 description coverage is 100%; both parameters are already well-described in the schema. The description adds no parameter-specific meaning beyond the schema, so 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?

Description clearly states the tool retrieves basic stock information by symbol and market, using specific verbs and resources. It also explicitly excludes cryptocurrency, differentiating it from crypto-related sibling 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?

Description implies when to use (for getting basic stock info) and explicitly states when not to use (for cryptocurrency). However, it does not mention alternatives among the many stock sibling tools, leaving usage guidance somewhat implied.

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 burden. It adds value by disclosing data source dependencies and fallback behavior, but lacks details on API key requirements, 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?

Two sentences, front-loaded with purpose, no redundant information. Every sentence adds value, 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?

Given no output schema and no annotations, the description is moderately complete but lacks information about return structure, pagination, or error handling. For a simple tool with few parameters, it is adequate but not thorough.

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 schema already documents both parameters. The description does not add extra parameter-specific meaning; it only mentions data sources, which is not param-related.

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 gets US stock insider trading records, with a specific verb ('获取') and resource ('美股公司内部人交易记录'). It is unique among siblings, as no other tool specifically covers insider trading.

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 mentions data source options (Alpha Vantage with API key, yfinance free), guiding when to use which. However, it does not explicitly state when not to use the tool or provide alternatives among siblings.

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

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

No annotations exist, so the description carries the full burden. It discloses the tool returns top holdings and changes, but omits details like data lag, sorting order, or limit effects. It does not contradict annotations (none present).

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

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

Two concise sentences efficiently front-load the core action and purpose. No redundant or extraneous text.

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 2-parameter tool without output schema, the description covers basic usage but lacks details on data source, limitations, or how it differs from similar sibling tools like stock_top10_holders.

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 baseline is 3. The description adds no extra meaning beyond what the schema already provides for 'date' and 'limit'. It doesn't clarify date format usage or limit impact.

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 'A-share fund heavy-position stock data' and displays top holdings and changes, effectively specifying the verb and resource. It distinguishes from sibling tools like stock_holder_num (shareholder count) and stock_top10_holders (top 10 holders) by focusing on fund holdings, though could be more explicit.

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 mentions 'for tracking institutional trends', implying usage context, but lacks explicit guidance on when not to use or alternatives. No exclusions or comparisons to similar tools are provided.

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, and the description does not disclose behavioral traits such as idempotency, rate limits, or response characteristics. For a read-only data tool, the description should at least imply it does not modify state, but this is not explicitly stated.

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 consists of two short sentences that deliver the core purpose without any redundant or extraneous 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 absence of an output schema and the complexity of the topic (dragon and tiger board), the description is minimal. It does not explain what the statistics include or how multiple data sources behave, leaving gaps for a new user. However, the parameter descriptions are sufficient.

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, so the schema already documents the parameters. The description adds no additional meaning beyond what the schema provides, hence baseline score of 3.

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

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

The description clearly states the tool retrieves statistics on individual stocks appearing in the dragon and tiger board (龙虎榜) from Shanghai and Shenzhen A-shares, with support for multiple data sources. This verb+resource combination distinguishes it from sibling tools like stock_realtime or stock_prices.

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. It does not mention any prerequisites, exclusions, or scenarios where sibling tools would be more 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?

描述仅为数据获取操作，无注解，但未声明副作用或权限需求。由于是只读操作，行为风险低，但未提供完整透明度（如速率限制、数据更新频率）。

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?

描述仅两句话，第一句明确功能，第二句提供背景。无冗余信息，但缺乏结构化（如使用列表或分段）。

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?

模式覆盖率100%，但无输出模式，描述未说明返回数据的结构或字段，对于理解完整的输入输出上下文不够完善。

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?

输入模式参数覆盖率100%，每个参数均有默认值和描述，但描述文本未额外添加语义，如日期格式的示例或limit的合理范围。基线为3，描述未超越模式本身。

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?

描述明确使用动词'获取'和'查看'，资源为'A股限售解禁日历'，并说明了功能（查看即将解禁的股票及解禁规模）。在兄弟工具中没有直接重复功能的工具，自然区分了目的。

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?

描述未提供任何使用时机或替代工具的指引，没有说明何时使用此工具而非兄弟工具，也没有排除情况。

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 fully disclose behavioral traits. It does not state that the operation is read-only, nor does it mention any side effects, authorization needs, rate limits, or data freshness. The description is too brief to cover these aspects.

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

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

The description is extremely concise and front-loaded: two sentences that first state the action and data included, then provide context about the indicator's significance. Every sentence adds value without redundancy.

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

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

There is no output schema, so the description should detail the return data structure. It only gives examples (融资余额、融券余额等) but not the full list of fields, types, or pagination. The agent lacks complete information about what to expect from the response.

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 for all three parameters. The description adds minimal value beyond the schema: it mentions that data includes 融资余额 and 融券余额, which is the content of the returned data rather than parameter details. Baseline 3 is appropriate as the schema already defines the parameters.

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 A-share market margin trading data including margin balance and short balance. It is specific about the resource (融资融券数据) and the action (获取). Among sibling tools, none explicitly target margin trading, so it is well-distinguished.

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. It does not mention any prerequisites, exclusions, or when it should be preferred over sibling tools like stock_fund_flow or stock_north_flow. Usage context is only implied by 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 carries the burden. It does not disclose any behavioral traits such as data source, update frequency, or limitations. It is minimally transparent but not misleading.

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 in Chinese, delivering the purpose and use case without waste. It is 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 tool has no parameters or output schema, and the description explains its core function well. However, it lacks details on the historical time range or the format of returned data, which could be helpful for agent usage.

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

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

There are no parameters, so the schema coverage is 100%. The description adds meaning by specifying that the tool retrieves PE/PB percentiles for the overall market, which is not evident from the empty schema.

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

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

The description clearly states it retrieves historical PE/PB percentiles for the overall A-share market, with a specific use case of judging market valuation. It is distinct from sibling tools like stock_industry_pe which focus on industry-level data.

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 says it is for judging overall market valuation, providing clear context. However, it does not mention when not to use it or suggest alternatives.

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 full burden. It only discloses the API key environment variable requirement. It does not mention rate limits, read-only nature, or any side effects. For a data retrieval tool, more behavioral context is needed.

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 only two short sentences, containing no unnecessary words. It is front-loaded with the core purpose and then the prerequisite, making it highly 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 lacks detail about the output format (e.g., news titles, sentiment scores), which is critical since no output schema exists. It also does not mention default behavior for the limit parameter or pagination. Given the tool's complexity (3 parameters, sentiment analysis), the description is insufficient.

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

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

Schema coverage is 100%, so the input schema already documents all three parameters. The description adds the API key requirement, which is helpful but not parameter-specific. It does not enhance understanding of parameter values beyond what the schema provides, thus 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 retrieves US stock news and sentiment analysis data, with optional symbol and topics parameters. It distinguishes itself from sibling tools like stock_news (likely Chinese) and stock_news_global (global) by specifying '美股' (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 does not provide any guidance on when to use this tool vs alternatives like stock_news or stock_news_global. It mentions an API key prerequisite but no exclusion criteria or context for choosing this tool over others.

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

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

No annotations are provided, so the description carries full burden for behavioral disclosure. It does not mention data freshness, rate limits, authentication requirements, or what happens on error (e.g., invalid indicator value). The description focuses solely on purpose, lacking 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 consists of two short, focused sentences. The first states the action and scope, the second adds context. No extraneous 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?

Without an output schema, the description should explain what the returned data looks like (e.g., time series, aggregation). It does not. Additionally, behavioral info is missing. For a simple tool like this, the description is adequate for core purpose but incomplete for a fully informed call.

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% coverage with a clear description of the 'indicator' parameter and its three options. The description adds no new meaning beyond the schema's default and enum values, so 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 retrieves north-bound capital flow data (沪深港通北向资金) including specific channels (沪股通, 深股通) and notes its significance as a bellwether. It uses a specific verb-resource pair ('获取...数据') and is distinct from sibling tools like stock_fund_flow or stock_sector_fund_flow_rank.

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 implicitly guides usage by noting that '北向资金是A股重要的风向标' (important bellwether), suggesting use for market sentiment analysis. However, it does not explicitly state when not to use or provide alternatives among siblings.

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

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

Without annotations, the description carries full behavioral disclosure burden. It adds value by explaining data source fallback behavior and API key requirement. However, it omits details like rate limits, caching, or error handling. This is above average but not complete.

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 efficient sentence, front-loaded with purpose, followed by data points and data sources. No redundant words; every clause adds value.

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

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

The description covers purpose, data points, and data sources. No output schema is provided, but the description lists expected outputs. It lacks mention of return format or pagination, but the tool is simple (single param). Minor gap prevents a 5.

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% (single parameter with clear description). The tool description does not add extra semantics to the parameter itself; it only contextualizes the output. Baseline score of 3 is appropriate as the description does not degrade or enhance parameter understanding.

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

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

The description clearly states the tool's purpose: '获取美股公司基本面概览' (get US stock company fundamental overview) and lists specific data points (market cap, PE, EPS, dividend yield, 52-week high/low, analyst ratings). This distinguishes it from sibling tools like stock_financials_us or stock_info, which focus on detailed financials or general info.

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 mentions data source fallback (Alpha Vantage with API key, then yfinance free), but does not explicitly state when to use this tool versus other stock sibling tools. Usage context is implied but not clearly articulated with when-not or alternative recommendations.

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

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

No annotations provided, so description carries full burden. It mentions supported markets (sh, sz) and periods but does not disclose read-only behavior, rate limits, or error handling for invalid symbols/unavailable 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?

Single sentence, front-loaded with key info (market, metrics, periods). No superfluous 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?

Without output schema, description sufficiently covers purpose and context for a simple multi-period statistics tool with two parameters. Could add return format, but not critical given low complexity.

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 already documents both parameters with examples and supported markets (100% coverage). Description adds period details but not param-level semantics beyond schema; 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 retrieves multi-period statistics (cumulative change, amplitude, turnover) for A-share stocks with specific period support (5,10,20,60,120 days). It distinguishes from siblings like stock_prices or stock_realtime by explicitly naming metrics and periods.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives such as stock_indicators or stock_screener. The description implies it's for multi-period stats but lacks explicit conditions 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?

No annotations are provided, so the description must disclose behavioral traits. It states the tool '获取' (fetches) data, implying a read-only operation, but omits details like rate limits, data freshness, or any constraints. For a simple data retrieval tool, this 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, well-formed sentence that conveys the core action and data categories without fluff. Every word serves a purpose, and the information is 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 simplicity of the tool (2 simple parameters, no output schema, no nested objects), the description sufficiently covers what the tool does. It could be improved by hinting at the output structure, but for its complexity level it is largely 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?

Input schema has 100% parameter description coverage, so the baseline is 3. The tool description does not add extra meaning beyond the schema's own descriptions of 'mode' and 'limit'. It reinforces the purpose but offers no new semantic 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 explicitly states the tool retrieves A-share equity pledge data, including industry statistics and market-level pledge ratios. The verb '获取' (get) and specific resource 'A股股权质押数据' clearly define the action and scope. Among siblings, no other tool covers equity pledge data, so it is well-differentiated.

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 other stock-related tools. It does not mention scenarios, prerequisites, or alternatives, leaving the agent to infer usage from context alone.

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 mentions automatic failover across multiple data sources and the limitation of no crypto support. However, it does not disclose whether the operation is read-only, rate limits, data freshness, or other behavioral traits. The description is adequate but not detailed.

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

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

The description is two sentences, front-loaded with the core purpose, and includes limitations and features without extraneous detail. Every word serves a purpose, achieving high conciseness.

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 could explain the return format. It mentions fetching prices and indicators but does not detail the structure or fields. Given the complexity of the tool (4 parameters, no output schema), the description is somewhat complete but lacks output details.

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

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

Schema coverage is 100%, so the input schema already describes all parameters thoroughly. The top-level description adds no extra parameter semantics beyond what is in the schema, such as reiterating the crypto restriction. According to the guidelines, with high coverage, 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 it retrieves historical stock prices and technical indicators based on symbol and market, and explicitly excludes cryptocurrencies. While it distinguishes from generic siblings, it does not explicitly differentiate from closely related tools like stock_prices_global.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for obtaining historical stock data with technical indicators, and explicitly states not to use for cryptocurrencies. However, it lacks guidance on when to use this tool versus alternatives like stock_realtime or stock_prices_global, and does not provide explicit when-not-to-use 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 provided; description only adds 'automated failover across multiple data sources' as a behavioral trait. Lacks information on read-only nature, permissions, or rate limits, leaving the agent uninformed.

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?

Extremely concise: two sentences. The first sentence delivers the core purpose, the second adds the failover feature. No unnecessary text.

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

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

Given no output schema, the description could mention return fields or data format. It only says 'historical prices and technical indicators'. The failover mention is useful, but overall completeness is adequate but not thorough.

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 each parameter well-documented (symbol format, market options, period, limit). The tool description adds no additional parameter meaning, so 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 fetches historical price data and technical indicators for US/HK stocks. The name 'global' and mention of US/HK differentiate it from other stock tools like stock_prices (likely A-shares), but no explicit 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?

No guidance on when to use this tool versus alternatives like stock_realtime or stock_prices. Does not specify prerequisites 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?

No annotations provided, so description carries full burden. It discloses auto failover across data sources (behavioral trait) but does not specify rate limits, permissions, data delay, or return format. Adequate but not detailed for a real-time feed.

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

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

Two sentences in Chinese, concise and front-loaded. No wasted words; every part provides 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?

Given no output schema, description reasonably covers returned fields (price, change, volume, turnover, PE). It omits timestamp and other common fields like open/high/low/close, and does not detail failover behavior. Mostly complete for its complexity.

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

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

Schema coverage is 100% with good descriptions for both symbol and market. The description adds value by listing output fields (price, change, volume, etc.), but does not enhance understanding of input parameters beyond schema. 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 it retrieves real-time market data for A-shares and Hong Kong stocks, listing key fields like latest price and change. It distinguishes from many sibling tools by specifying real-time quotes and mentioning multi-source failover, but lacks explicit contrast with similar tools like stock_batch_realtime or stock_prices.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives. The description does not mention scenarios, exclusions, or related tools. An agent would have no context to distinguish from siblings like stock_batch_realtime for batch queries or stock_prices for historical data.

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

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

No annotations provided. Description does not disclose whether the tool is read-only, rate limits, data freshness, or other behavioral traits. For a screening tool, read-only nature is assumed but not stated.

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

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

Single sentence, no redundancy, front-loaded with purpose. Every word contributes meaning.

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

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

No output schema, but description does not explain return format, pagination, or sorting behavior. With 13 parameters and no annotations, more contextual detail is needed for completeness.

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?

All 13 parameters have descriptions in the schema (100% coverage), so baseline 3. The description adds no extra parameter level details beyond listing indicators, but it does summarize the filtering dimensions.

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

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

Description clearly states the tool screens A-share stocks with specific indicators (PE, PB, market cap, etc.), distinguishing it from real-time price or news tools among 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?

Description implies usage for filtering stocks by criteria but does not specify when to use versus alternatives like stock_realtime or stock_indicators, nor provide 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?

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 full responsibility for behavioral disclosure. It mentions input and general output but omits details such as data freshness, market coverage (only A-shares, confirmed), rate limits, or whether it requires specific data subscriptions.

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 efficiently conveys the tool's purpose. It is front-loaded with the key action and resource, containing no unnecessary words or redundancy.

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

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

Given the tool's simplicity (one parameter, no output schema), the description is adequate but not complete. It does not specify the format of the returned sector information (e.g., list of names, codes) or any limitations, leaving some ambiguity 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 already provides a detailed description for the single 'symbol' parameter, including format examples. The tool description adds no additional semantic value beyond the schema, 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 title and description clearly state the tool's purpose: retrieving industry and concept sector information for A-share stocks. The verb '获取' and resource '个股所属板块' are specific, and the tool distinguishes itself from siblings like stock_sector_fund_flow_rank and stock_board_cons by focusing on individual stocks' sectors.

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 only states the tool's function without providing guidance on when to use it or when alternatives (e.g., stock_board_cons for sector constituents) might be more appropriate. Usage is implied but not explicitly clarified.

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

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

No annotations are provided, so the description bears full responsibility. It only reveals the API key prerequisite. No disclosure of read-only nature, rate limits, data freshness, or error behavior. For a data retrieval tool, more transparency is needed.

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—one sentence for purpose plus one for prerequisite. Information is front-loaded. Could be slightly more structured (e.g., separate lines for purpose and requirements), but overall 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?

Given 5 parameters, 100% schema coverage, no output schema, and no annotations, the description covers basic purpose and a prerequisite but lacks details on return format, errors, or usage context. Adequate 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?

Schema coverage is 100%, so baseline is 3. The description adds the API key requirement, which is not in the schema, but does not elaborate on parameter usage beyond listing examples. Minimal added value over schema.

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

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

The description clearly states the tool fetches technical analysis indicators for US stocks, listing examples (SMA, EMA, RSI, MACD, BBANDS). It is distinct from sibling tools like 'stock_indicators' (non-US) and price 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 implies usage for US stock technical analysis but does not explicitly state when to use this tool vs alternatives, nor when not to use it. The API key requirement is mentioned but no comparative guidance.

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

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

With no annotations, the description carries the burden of disclosing behavior. It correctly identifies the operation as retrieving data, but it does not mention any specific behavioral traits such as whether the data spans multiple periods or requires authentication. A score of 3 reflects adequate but minimal transparency.

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

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

The description is a single sentence that conveys the core functionality and purpose. It is concise and front-loaded, but could benefit from a slightly more structured breakdown of parameters.

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 3 parameters, yet the description does not mention the return format or that the 'limit' parameter controls multiple periods of data. The context provided is insufficient for an agent to fully understand the tool's behavior without additional inference.

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?

Although the input schema has 100% description coverage, the description's mention of 'A-shares' conflicts with the symbol parameter that accepts foreign stocks like AAPL. This contradiction reduces clarity, and the description adds little beyond what the schema already provides.

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 it retrieves top 10 shareholders or tradable shareholders for A-share stocks and specifies the purpose of analyzing equity structure and institutional holdings. It is specific enough to distinguish from generic tools, though it does not explicitly differentiate from sibling tools like stock_institutional_holdings.

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 or prerequisites mentioned. The description merely states what the tool does without contextual usage advice.

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 exist, so the description carries full burden. It states the output includes PE/PB and industry percentile, but lacks details on output structure, side effects, or authentication needs. Since it's a read-only data retrieval, the description is adequate but not thorough.

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 Chinese sentence that efficiently conveys the tool's purpose and scope, with no extraneous information. It is front-loaded and immediately clear.

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 (one parameter, no output schema), the description covers the core function. It mentions the returned metrics (PE/PB percentile), which helps an agent understand the output. However, without an output schema, complete details are absent, but for a straightforward tool, this is nearly sufficient.

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

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

Schema description coverage is 100% for the single parameter 'symbol', which includes format examples. The tool description adds context that the tool is A-share focused, aligning with the parameter. No additional semantic enrichment beyond the schema is provided, meeting the baseline for high 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 provides valuation comparison data for individual A-share stocks, including PE/PB percentiles within their industry. This differentiates it from sibling tools like stock_industry_pe (industry-level) and stock_market_pe_percentile (market-level), using specific verb '获取' and resource '估值对比'.

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 individual stock valuation relative to industry, but does not explicitly state when to use this tool versus alternatives (e.g., for US stocks, use stock_overview_us; for industry averages, use stock_industry_pe). No conditions or exclusions are provided.

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

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

With no annotations, the description carries full burden for behavioral transparency. It only states 'get data' without disclosing effects, rate limits, or whether the operation is read-only. This is insufficient.

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, clear sentence with no extraneous information. It is front-loaded and concise, effectively conveying the core function.

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 3 parameters, lack of output schema, and no annotations, the description is too brief to fully inform an agent. It omits return format, data freshness, and potential constraints, leaving gaps in 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?

Schema coverage is 100%, so the description adds little beyond what the schema already provides. The mention of 'limit-up or strong pool' hints at the pool_type parameter, but does not add new meaning or usage details.

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

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

The description clearly states it retrieves Chinese A-share limit-up or strong stock pool data from Shanghai and Shenzhen exchanges, using a specific verb and resource. However, it does not differentiate from sibling tools like stock_lhb_ggtj_sina which may offer similar data.

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 any prerequisites or context for when it is appropriate. The description lacks any usage recommendations.

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

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

No annotations provided, so description bears full burden. It names the indicators used but does not disclose output format, data freshness, failure modes, or if signals are real-time. This is adequate but not thorough for a synthetic indicator 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?

Single sentence, directly to the point, contains all necessary functional information 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 tool generating trading signals with no output schema, the description covers the core logic but lacks specifics on output shape (e.g., buy/sell/hold, signal strength). Adequate but leaves room for improved completeness.

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 describes both parameters (symbol and days) with 100% coverage. The description adds no extra semantic value beyond the schema, so baseline 3 applies.

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

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

The description clearly states the tool generates stock trading signals based on technical indicators (MACD, KDJ, RSI, Bollinger Bands). It specifies the verb 'generate' and the resource 'trading signals', and it distinguishes from sibling tools by focusing on multi-indicator synthesis.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies use for judging buy/sell timing via technical analysis. It provides clear context but omits explicit exclusions or alternatives when other stock analysis tools (e.g., stock_indicators) might be more appropriate.

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