QuantSonar

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

With no annotations provided, the description carries the full burden. It discloses the PRO requirement and 403 error, and specifies the return format as a JSON array with fields. However, it does not mention pagination, rate limits, or error handling beyond the 403 case, leaving some behavioral traits undisclosed.

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 well-structured and front-loaded with purpose and requirement, followed by args and returns. It is concise and avoids unnecessary prose, though the argument list duplicates schema descriptions, which is a minor 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?

The description includes purpose, parameters, return fields, and authentication requirement, covering essential details. It lacks guidance on edge cases (e.g., invalid symbol handling, date range validation) and does not explain the meaning of the adjustment factor metric, leaving gaps for less familiar users.

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 descriptions cover all four parameters at 100% coverage, so the description adds no incremental semantic value by repeating them. The trade_date exclusivity note is already present in the schema, meaning the description is redundant but not harmful, fitting the baseline 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 returns adjustment factors (复权因子) for market data, with concrete examples like 000001.SZ. It does not explicitly differentiate from the sibling `etf_adj_factor`, but the name and example imply stock context, which is a minor gap.

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 like `etf_adj_factor` or `daily`. The only usage-related note is the PRO plan requirement and 403 error for lower tiers, which is a licensing constraint rather than contextual selection 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?

No annotations are provided, so the description must disclose behavior. It discloses an error condition (403 on low-tier), the output format (JSON array with specific fields), and the data content. It does not state whether the operation is read-only (obvious) or discuss potential side effects, rate limits, or behavior with missing parameters. Some transparency is present but not comprehensive.

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 efficiently structured with a clear purpose statement, a prerequisite note, then Args and Returns sections. The long list of return fields is necessary for clarity. No redundant or filler content is present.

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 three optional parameters, fully documented in the schema, and an output field list covering all return values, the description is fairly complete. It lacks a note about behavior when no parameters are provided (since all are optional), and it does not explain how the date range works, but these are minor gaps. The PRO requirement adds important context.

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

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

The schema covers 100% of parameter descriptions, and the Args section in the description exactly repeats those descriptions without adding extra meaning (e.g., relationships between parameters, formatting examples, or default behavior). Baseline 3 is appropriate because the schema already documents the parameters sufficiently.

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

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

The description states the tool's purpose: '券商研报盈利预测（财务数据）' (broker research report earnings forecast financial data), which clearly indicates it provides analyst report forecast data. It does not use an explicit verb like 'retrieve' but the intent is unambiguous. It is distinct from siblings like 'forecast' or 'fundamentals' by focusing on broker research reports.

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 a prerequisite (PRO package) and that low-tier calls return 403, which is useful. However, it does not explicitly state when to use this tool over alternatives or provide exclusion criteria. The usage context is only implied by the tool's name and subject matter.

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. It discloses the subscription requirement and error behavior (403 for low-tier), and also specifies the return format as a JSON array with named fields. This is meaningful transparency 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 compact and well-structured, front-loading the purpose and then listing parameters and return fields in a clear format. No filler words. It is slightly verbose with the args list but earns its place.

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

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

Given the tool's moderate complexity (5 optional params, output schema exists), the description covers the essential aspects: all parameters, return fields, and the PRO access requirement. It is complete enough for an agent to invoke the tool correctly.

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

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

The input schema already has 100% parameter coverage with descriptions. The description repeats the same parameter descriptions and examples (e.g., period format), adding little beyond what the schema already provides. 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 identifies the tool as providing financial audit opinions (财务审计意见) for financial data, which distinguishes it from sibling tools like balance_sheet or income. The resource is clear, though the verb is implicit (implied 'get' or 'retrieve') rather than 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 provides a key prerequisite: requires PRO or higher package, with low-tier calls returning 403. This helps set expected usage context but does not give explicit guidance on when to prefer this tool over alternatives or when not to use it.

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

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

No annotations are provided, so the description carries the burden. It discloses the PRO requirement and that low tiers receive 403, and specifies the output is a JSON array with a list of fields. However, it does not describe data source, refresh frequency, or parameter dependency 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?

The description is well-structured with distinct sections for requirement, arguments, and returns. However, the Args section duplicates the schema descriptions, adding redundancy. The returns field list is informative but lengthy, making it slightly less concise.

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 includes the output fields and the PRO requirement, adding context beyond schema. However, it does not clarify parameter combinations, whether symbol is required, or data granularity (quarterly vs annual). The output schema exists but isn't shown; the description's field list helps moderately.

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

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

Schema coverage is 100% with descriptions for all parameters. The description's Args section repeats the schema information almost verbatim (e.g., period description identical), adding no new parameter semantics. 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 identifies the tool as '资产负债表' (balance sheet) and lists the return fields, making the resource clear. It distinguishes from sibling tools like income and cash_flow by its specific financial statement focus, though it lacks an explicit verb like 'get' or 'query'.

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 state when to use this tool versus alternatives, only mentions the PRO plan requirement. The tool name and return fields imply use for balance sheet data, but no explicit when/when-not criteria or alternative comparisons 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?

The description discloses the return format (JSON array) and the exact fields returned, which provides some transparency beyond the schema. However, it does not explicitly state that the tool is read-only, nor describe behavior when filters are omitted or when no matches are found.

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 compact and well-structured with clear 'Args' and 'Returns' sections. The only minor redundancy is the repeated 'ETF' in the title and parenthetical.

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 all parameters and return fields, which is useful for a query tool. However, it does not explain the default query behavior when all parameters are optional, nor how multiple filters interact, leaving some ambiguity for a user trying to construct a valid request.

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

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

The input schema already describes all five parameters with identical descriptions. The description's Args section simply repeats the schema content, adding no new semantic information. Since coverage is 100%, 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 identifies the tool as providing ETF basic information (ETF 基础信息) and lists filtering parameters, but lacks an explicit verb like 'query' or 'get', and does not distinguish it from sibling tools like 'stocks' or 'etf_daily'.

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 instead of alternatives such as etf_daily or tracking_indices. The description only lists parameters without any context about use cases 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?

Without annotations, the description carries the burden and discloses key behaviors: PRO plan mandatory (403 otherwise), return format as JSON array, specific fields, and the mutual exclusivity of trade_date with start/end. This goes beyond a simple 'get' declaration.

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 compact, front-loaded with purpose and access requirements, followed by structured Args and Returns sections. No unnecessary prose.

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 data retrieval tool with an output schema present, the description covers access constraints, all parameters, and return fields. It is sufficient for the agent to invoke correctly, though it omits potential details like pagination or date-range behavior.

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

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

Schema description coverage is 100% and the description repeats the same parameter descriptions (e.g., '证券代码（带后缀）'), adding no new meaning beyond the schema. Baseline 3 applies.

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

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

The description clearly identifies the resource as block trade market data (大宗交易行情数据). It distinguishes from siblings by naming a unique data category, though it lacks an explicit verb like 'get' or 'list'.

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. It mentions a PRO plan requirement and the trade_date mutual exclustivity constraint, but these are prerequisites, not usage context.

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

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

With no annotations present, the description carries most of the behavioral disclosure burden. It does disclose the premium tier requirement and the JSON array return format, but it omits important details such as whether parameters can be omitted, how filters combine, pagination behavior, or other error conditions beyond 403.

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 well-structured and readable, with a clear title, access note, Args list, and Returns list. However, the Args section duplicates the input schema verbatim, adding redundancy and length without new information.

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

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

The description covers access requirements, parameter formats, and return fields, and the schema handles individual parameters. Still, it lacks guidance on which parameter combinations are valid, the meaning of all parameters being optional, and the partial enum for report_type, which could leave an agent uncertain when constructing a 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?

Schema description coverage is 100%, and the parameter descriptions in the schema exactly match the Args section. The description adds no additional semantic meaning beyond what the schema already provides, so it does not push above 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 identifies the tool as providing cash flow statement financial data and lists the return fields, making the resource and scope evident. However, it lacks an explicit verb such as 'retrieve' or 'query', and it does not explicitly differentiate itself from sibling tools like income or balance_sheet.

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

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

There is no guidance on when to use this tool versus alternatives. The only contextual note is the PRO+ subscription requirement and the 403 response for lower tiers, which is an access constraint rather than a usage guideline.

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 takes on full responsibility. It discloses the subscription requirement and the resulting 403 response, which is important behavioral context. It also outlines the return format as a JSON array with specific fields, adding clarity beyond the schema. It does not discuss limitations like pagination or rate limits, but for a read-only list tool, this is adequate.

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 well-structured with a one-line summary, a requirement line, and clearly labeled 'Args' and 'Returns' sections. It is reasonably concise for a tool with five parameters. However, the Args section repeats schema information, which is slightly redundant, but the overall organization is clear 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?

Given the tool's complexity (five optional parameters, no required fields), the description is fairly complete. It covers the subscription constraint, parameter usage (mutual exclusivity), and return fields. An output schema exists, so the description doesn't need to detail every field. Missing are concrete usage examples, but they are not essential for a straightforward data retrieval tool.

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

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

Schema coverage is 100%, so all parameter descriptions are present in the input schema. The Description's 'Args' section essentially duplicates the schema descriptions without adding new meaning, such as examples, relationships between parameters, or value constraints. Therefore, it meets the baseline but does not exceed it.

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

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

The description clearly states the tool returns '概念/题材板块成分股（行情数据）' (concept/theme sector constituent stocks with market data), which identifies the specific resource and the type of operation. It distinguishes itself from sibling tools like 'concepts' (which likely lists concepts) by focusing on members. However, it does not explicitly contrast itself with similar tools, so it falls short of a 5.

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 usage constraints: it notes that PRO or higher subscription is required (lower tiers get a 403) and explains that trade_date is mutually exclusive with start_date/end_date. However, it gives no guidance on when to choose this tool over alternatives, nor does it mention any exclusions or preconditions beyond the subscription level.

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 and does well: it discloses the PRO plan requirement, the 403 error for lower tiers, the mutually exclusive nature of trade_date with start/end dates, and the exact return format with field names. This goes beyond minimal behavioral disclosure, though it does not cover rate limits or data update frequency.

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 well-structured: a clear title, a permission note, an Args list, and a Returns list. It is front-loaded and every line serves a purpose. It repeats schema information, which adds some redundancy, but the organization makes it easy to scan.

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 data retrieval tool with 6 optional parameters and an output schema (return fields listed), the description covers purpose, parameters, permissions, and return format. It lacks explicit guidance on alternative tools and does not explain the meaning of fields like 'leading' or 'level', but overall it provides enough context for correct invocation.

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

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

Input schema has 100% description coverage for all 6 parameters, so the baseline is 3. The description duplicates the schema's parameter descriptions but adds no new meanings beyond what is already in the schema. The Returns section adds value for output understanding, but not for parameter semantics specifically.

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

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

The description clearly identifies the resource as '概念/题材板块指数（行情数据）' (concept/theme sector index market data), which distinguishes it from generic market data tools. However, it lacks an explicit verb like 'query' or 'retrieve', and does not directly compare to sibling tools like concept_members or industries, so it does not fully meet the 5-level standard.

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 a usage constraint (requires PRO plan, returns 403 otherwise) but gives no guidance on when to choose this tool over alternatives. There is no mention of when to use concepts vs. concept_members, index_daily, or industries, nor any exclusions or contextual 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?

With no annotations, the description carries the full burden. It does disclose the return shape (JSON array with listed OHLC fields) and the mutually exclusive date modes, adding useful behavior context. However, it omits whether symbol is required, how empty results are handled, pagination or limits, and doesn't explicitly state that this is a read-only market data 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?

The description is compact, well-structured, and front-loaded: a one-line summary, an Args list, and a Returns list. Every line earns its place, with no redundancy 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 straightforward OHLC endpoint, the description covers the parameter modes and return fields well. However, the input schema marks all parameters as optional while the description never clarifies that symbol is effectively required or what happens when no dates are supplied, leaving a meaningful gap for an agent building a valid request.

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 parameter names and formats are already documented. The description adds meaningful semantics by explaining the trade_date exclusivity with start/end dates and by giving a concrete symbol example with the suffix format, going beyond the schema's terse 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 opens with '日线行情（OHLC）' clearly identifying daily OHLC market data and lists the returned fields, making the resource unambiguous. However, it lacks an explicit verb like 'get' and does not explicitly differentiate itself from siblings such as etf_daily or index_daily, so it doesn't fully stand apart.

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 a useful in-parameter note that trade_date is mutually exclusive with start/end dates, which guides invocation. But there is no mention of when to use this tool versus its many siblings (etf_daily, index_daily, fx_daily), no alternative recommendations, and no exclusions or conditions for use.

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 discloses the plan requirement and error behavior for low-tier calls, and describes the return fields. However, it does not mention behavior when all parameters are omitted, sorting, or other potential side effects.

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 well-structured with a title, requirement note, args, and returns. It is relatively concise but repeats schema information, making it slightly redundant.

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?

An output schema exists and the description lists return fields, but it lacks details on parameter interactions, such as what happens if only symbol is provided or if no dates are specified. The optional nature of all parameters is unexplained.

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's Args section duplicates the schema descriptions exactly, adding no new meaning. The baseline of 3 applies as the schema already documents all 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 identifies the tool as a financial disclosure calendar and clearly states it returns a JSON array of disclosure dates. However, it lacks an explicit verb like 'get' or 'list', and does not differentiate from 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?

The only usage guidance is the PRO requirement and 403 error for lower tiers. It implies usage for retrieving disclosure dates but offers no explicit comparison to alternatives or when-not-to-use conditions.

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 discloses some behavioral traits: expected output fields and a 403 error for insufficient plan level. However, it does not state that this is a read-only operation, nor does it mention pagination, rate limits, or data freshness. For a data query tool this is acceptable 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 efficiently structured with a title, permission note, a compact Args block, and a Returns block. Every sentence earns its place, though the Args section duplicates schema content. Overall it is concise and easy to scan.

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 four optional parameters, the description fails to clarify that a symbol and at least one date (either start/end range or trade_date) are effectively required for a useful call. It mentions trade_date exclusivity but does not state that one date form must be chosen. This ambiguity leaves the agent under-informed about how to construct a valid request.

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

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

The input schema already describes all four parameters with 100% coverage, so baseline is 3. The description's Args section merely repeats the schema's descriptions without adding new information. It does not clarify defaults, precedence, or additional combination rules beyond what the schema already states about trade_date exclusivity.

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 identifies the resource as '筹码分布与胜率' (chip distribution and win rate) and gives detailed fields and parameters. This distinguishes it from typical financial data siblings like daily or indicators, though it lacks an explicit verb like 'retrieve' and a formal comparison with alternative 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?

It clearly states the subscription requirement ('需要 PRO 及以上套餐') and the consequence of unauthorized use ('低档位调用返回 403'). However, it provides no explicit 'when to use' guidance relative to alternatives and no concrete exclusions. Usage is mainly implied by the tool name and returned fields.

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 the PRO requirement, 403 behavior, and return format, but does not explicitly state read-only semantics or other side effects.

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?

Structured Args/Returns with clear sections and no filler beyond a repeated title phrase; the PRO requirement 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?

The description is adequate for a straightforward query tool and includes access requirements and return fields, but lacks parameter combination guidance and doesn't compensate for the absence of annotations.

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 baseline is 3. The description merely repeats the schema parameter descriptions without adding relationships, required combinations, or format constraints.

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 identifies the resource (dividend/bonus share data) and enumerates return fields, making the tool's function inferable. However, it lacks an explicit verb like 'query' or 'retrieve' and does not distinguish from sibling tools such as 'distribution'.

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?

Only a PRO tier requirement and 403 error are mentioned; no guidance on when to prefer this tool over alternatives or how to combine parameters.

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 behavioral transparency burden. It discloses output fields and the trade_date exclusivity rule, which is useful. However, it does not explain what happens when symbol is omitted, whether date ranges are limited, or what the adj_factor values represent, leaving meaningful behavioral ambiguity.

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 compact and front-loaded, with a clear Args/Returns structure and no filler prose. The opening 'ETF 复权因子（ETF）' is slightly redundant, but overall every line is purposeful and appropriately sized.

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?

An output schema exists, so the description does not need to fully explain return values, and it does cover parameter formats and the exclusivity constraint. However, with all parameters optional, it leaves open questions about required inputs, default date ranges, and query limits, which are important for a financial data endpoint.

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

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

Schema description coverage is 100%, and the description's Args section largely repeats the schema's parameter descriptions. It adds no new semantics beyond what the schema already states, so the baseline of 3 applies. The 'with start/end mutually exclusive' detail is already present in the schema too.

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 identifies the specific resource (ETF adjustment factor) and lists the output fields, making its purpose clear. It does not use an explicit verb like 'retrieve', but the Args/Returns structure implies a data-fetching tool. The mention of 'ETF/板块/外汇/港股等' helps distinguish it from general stock adjustment factor tools, though it does not explicitly name sibling alternatives.

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

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

No guidance is given for when to choose this tool over alternatives such as adj_factor or etf_daily. The only usage-related note is that trade_date is mutually exclusive with start_date/end_date, which is a parameter constraint, not tool-selection guidance. This leaves the agent to infer applicability from the name and sibling list.

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

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

With no annotations, the description carries the burden of explaining behavior. It discloses the return format (JSON array with specific fields), the mutual exclusivity of trade_date against start/end, and the exact-match behavior for symbol. It does not mention any side effects, permissions, or rate limits, but for a read-only data query, this is adequate. The description adds value by detailing the output fields.

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 and well-structured, with a clear title, an Args section, and a Returns section. Every line is informative and there is no redundancy or fluff. It fits the tool's simplicity.

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 essential aspects: what it does, the parameters, and the return structure. However, it lacks explicit guidance on when to use this tool vs similar daily-data tools, and it doesn't mention any pagination or limits. Given that an output schema is present (as indicated), the return field list is a helpful addition, but the absence of usage differentiation makes it slightly 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?

All four parameters already have descriptions in the schema, and the description largely repeats those descriptions verbatim. It adds no new semantic meaning beyond the schema, such as examples or edge cases. The only additional context is the explicit note that trade_date is mutually exclusive with start/end, which is already present in the schema's trade_date description. Therefore, the description adds minimal value over 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 'ETF 日线行情（ETF）' which identifies the tool as providing daily market data. It further specifies that the symbol can be ETF, sector, forex, HK stock, etc., giving a broad but clear scope. However, it does not explicitly distinguish itself from sibling tools like 'daily' or 'index_daily', so it's not fully 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 a usage pattern with parameters, implying it is used for retrieving daily market data for a given symbol over a date range or single date. It does not explicitly state when to prefer this tool over alternatives such as 'daily' or 'fx_daily', nor does it list any exclusions. Therefore, usage guidance is only 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?

Without annotations, the description carries the transparency burden. It discloses the entitlement requirement ('需要 PRO 及以上套餐') and the resulting 403 error, which is useful behavioral context. It also states the return type and field list. However, it doesn't mention potential pagination, empty results, or other error cases, leaving gaps in behavioral 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 well-structured and efficient: a one-line purpose, a single critical requirement note, a bulleted Args list, and a Returns field list. Every element serves a purpose, and the format is scannable. The length is justified by the return field enumeration, which provides concrete output expectations.

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 aspects: purpose, parameters, returns, and a subscription constraint. However, it leaves important usage details ambiguous, such as whether symbol is required (though schema says optional), and how start_date/end_date interact with period. The output schema exists but is not shown; the return field list partially compensates. For a tool with no annotations, this is 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%, and each parameter already has a description in the schema. The description repeats these descriptions verbatim (e.g., 'symbol: 证券代码（带后缀）') without adding new meaning. It does not clarify relationships between parameters (e.g., whether period supersedes start_date/end_date). Since the schema already documents all parameters, the baseline of 3 applies, with no added 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 opens with '业绩快报（财务数据）' which clearly identifies the resource as financial express report data. The Returns section further specifies it returns a JSON array with financial fields, making the tool's function clear. However, it lacks an explicit verb like 'query' or 'retrieve' and does not differentiate from sibling tools such as 'forecast' or 'indicators', so it's not a perfect 5.

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

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

The only usage-related information is the PRO-tier requirement with a 403 error note, which is a prerequisite rather than a usage guideline. There is no mention of when to prefer this tool over alternatives like 'forecast' or 'income', nor any exclusions or context for which scenarios this tool is designed for. The description implies use for express financial reports but provides no explicit 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?

No annotations are present, so the description carries the disclosure burden. It reveals the PRO requirement and 403 error for lower tiers, and outlines return fields. However, it doesn't disclose rate limits, data update frequency, or any side effects, though the read-only nature is implied.

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 compact with clear sections for Args and Returns. It earns its place without excessive verbosity, though some repetition of schema descriptions is slightly redundant.

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 5-param data retrieval tool with an output schema, the description covers the essentials: authentication, parameters, and return fields. Yet it omits guidance on parameter combinations (e.g., whether period is required) and data coverage limits, leaving some ambiguity for agents.

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 simply repeats the same parameter definitions as the schema (e.g., symbol, start_date, period). It adds no new semantic information beyond the schema, so the 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 identifies the tool as providing financial earnings forecast data ('业绩预告'), with parameter and return details. It distinguishes from sibling data tools by its specific focus on forecast announcements, though it lacks an explicit verb like 'retrieve'.

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 vs alternatives like 'express' or 'indicators'. It only states the PRO tier requirement, which is an access constraint rather than a usage guideline.

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. It discloses the PRO plan requirement and the 403 error behavior for lower tiers, and lists the return fields in the Returns section. However, it does not mention pagination, rate limits, or any other behavioral nuances, so transparency is partial.

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 well-structured with a summary line, a requirement note, and clearly labeled Args/Returns sections. It is concise overall, though listing all return fields is somewhat verbose and likely redundant given the presence of an output schema, but it does not harm clarity.

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 data retrieval tool with four well-documented parameters and a returned field listing, the description is mostly complete. The main gap is the lack of differentiation from sibling tools like 'daily' and 'indicators', which would help the agent decide when to invoke this particular tool.

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

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

Schema coverage is 100%, but the description adds meaning by explicitly stating that trade_date is mutually exclusive with start_date/end_date, and provides usage examples (e.g., symbol with suffix). This goes beyond the schema descriptions and helps the agent construct valid parameter combinations.

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 this tool provides daily fundamental indicators (PE/PB/turnover rate/market cap) for securities, which distinguishes it from generic daily price data or technical indicator tools. However, it lacks an explicit verb like 'get' or 'retrieve', making the purpose slightly less direct than ideal.

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 the PRO plan requirement and the mutual exclusivity of trade_date with start/end_date, but it provides no guidance on when to use this tool instead of siblings like 'daily', 'indicators', or 'technical_factors'. No alternatives are named, so the agent cannot easily choose between this and 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?

With no annotations, the description provides useful behavior beyond the schema: it notes '代码原样匹配' (exact symbol matching), the default symbol, and the mutual exclusivity of trade_date with start/end dates. It also lists the exact return fields. This is sufficient for a simple data retrieval tool, though it doesn't cover pagination, rate limits, or data availability.

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 tightly structured with separate Args and Returns sections, each line serving a purpose. It avoids fluff and front-loads the core purpose. Length is appropriate for the tool's complexity.

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

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

For a low-complexity data retrieval tool, the description covers all essential invocation details: what data is returned, the default symbol, and how to specify dates. The output schema exists, so listing fields is redundant but helpful. The only gap is the lack of sibling differentiation, but that's not required for correct usage.

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

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

Schema description coverage is 100%, so the baseline is 3. The description repeats the parameter meanings and default values without adding new semantics. It does organize them cleanly, but no additional nuance 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 opens with '外汇日线（默认 USDCNH）' (Forex daily line, default USDCNH), clearly identifying the tool as providing forex daily OHLC data. This is specific and distinguishes it from sibling daily tools like 'daily' or 'etf_daily', which cover stocks and ETFs respectively.

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 forex data via the asset class in the title and default symbol, but it does not explicitly state when to use fx_daily versus alternatives like daily or etf_daily. It also doesn't mention any exclusion criteria or scenarios where another tool would be preferred.

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

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

With no annotations, the description carries the burden and does disclose that lower-tier plans return 403, which is a meaningful failure mode. It also lists the return fields, giving the agent expectations about the output. It doesn't mention rate limits or pagination, but the plan requirement is substantial context.

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

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

The description is compact and well-structured: a one-line purpose, plan constraint, Args list, and Returns list. It front-loads the purpose and every section serves a distinct 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?

For a moderate query tool with 5 optional parameters, the description covers the plan requirement, parameter formats, and return schema. It lacks explicit defaults for omitted parameters, but the overall information is sufficient for an agent to invoke the tool correctly.

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

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

The input schema already describes all 5 parameters with 100% coverage, so the description's parameter section adds little beyond repeating the schema. It does provide an example symbol format and enumerates the allowed values for trade_type and holder_type, which matches the schema's descriptions.

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

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

The description clearly identifies the tool as querying "重要股东增减持（行情数据）" (major shareholder increase/decrease holdings market data), which is a specific resource and distinguishes it from siblings like shareholders or block_trade. The verb is implied (query/list), but the resource is unmistakable.

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 notes the PRO+ plan requirement and 403 error for lower tiers, giving a concrete precondition. However, it doesn't explicitly state when to choose this over sibling tools like shareholders or top_list, nor does it provide exclusion criteria.

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 explicitly discloses a critical behavior: lower-tier subscriptions receive a 403 error. It also describes the return format (JSON array with specific fields). However, it does not mention optionality of parameters or interaction between them, which is a minor 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 includes extensive lists of parameters and return fields that are already captured in the input and output schemas. This redundancy makes the description longer than necessary. Only the PRO requirement sentence adds unique value, so the description is not concise.

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 8 optional parameters and an output schema, so the description does not need to explain return values. It correctly names the resource and adds the PRO requirement. However, it offers no guidance on parameter combinations or typical usage scenarios, and does not differentiate from sibling financial statement tools, leaving the description only minimally complete for a complex 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 Args section in the description merely restates the exact descriptions already present in the input schema, which has 100% coverage. No additional semantic value is added beyond what the schema provides, so the baseline score of 3 is appropriate.

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

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

The description opens with '利润表（财务数据）' which clearly identifies the tool as providing income statement financial data. This distinguishes it from sibling tools like balance_sheet and cash_flow. However, it lacks an explicit action verb such as 'get' or 'retrieve', making the purpose slightly less direct.

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

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

The only usage-related guidance is the PRO subscription requirement ('需要 PRO 及以上套餐'). There is no mention of when to prefer this tool over alternate financial statement tools like balance_sheet or cash_flow, nor any exclusions 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?

With no annotations, the description carries the burden of disclosing behavior. It discloses the return format and field list, which is useful. However, it does not mention potential pitfalls like date range coverage, data update frequency, or error behavior. It provides basic transparency but lacks richer context.

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

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

The description is extremely concise, with the main purpose front-loaded and parameters/returns presented in a clear, structured list. Every line adds necessary 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 simple data-retrieval tool, the description covers the essential aspects: purpose, parameters, and return fields. It lacks clarity on default date range when no dates are supplied and on data update behavior, but these are minor gaps given the tool's simplicity and the presence of an output schema.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds value by explicitly noting that 'trade_date' and 'start_date'/'end_date' are mutually exclusive ('二选一'), which is not enforced in the schema. It also restates parameter formats clearly.

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

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

The description clearly identifies the tool as returning daily index data, specifically for the Shanghai Composite Index by default. It distinguishes from sibling tools like 'daily' (stock data) and 'etf_daily' by specifying '指数' (index) and the default symbol. The 'Returns' section confirms the data 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?

No guidance is provided on when to use this tool versus alternatives such as 'daily' or 'etf_daily'. The description only states parameters and returns but does not mention exclusions or compare with siblings. The usage context is only implied by the tool name and description.

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

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

The description discloses a key behavioral trait: '需要 PRO 及以上套餐（低档位调用返回 403）' (requires PRO and above, lower tiers return 403). It also notes the mutual exclusivity between trade_date and start/end dates ('与 start/end 二选一'). However, with no annotations, it doesn't cover other behaviors like rate limits, pagination, or default behavior when no dates are provided.

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

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

The description is short and has a clear structure (title, auth note, Args, Returns). However, the Args block duplicates the schema property descriptions, so those lines don't earn their place. The Return fields section is useful, but overall conciseness is marred by redundancy.

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

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

Given the tool has an output schema and the description covers auth constraints, return format, and the trade_date/start_date exclusivity, it is reasonably complete. But it doesn't clarify optionality of all parameters or what happens if no date filter is used. For a straightforward data retrieval tool, this is adequate but not rich.

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

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

The schema already provides 100% coverage of parameter descriptions, and the Args section in the description simply repeats the same text. No additional semantic value is added beyond the schema. The mutual exclusivity is already in the schema, so the description doesn't compensate 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 resource: '指数成分与权重' (index constituents and weights). The Returns section lists relevant fields (symbol, trade_date, con_symbol, con_name, weight), which reinforces the purpose. It is distinct from siblings like index_daily, though it doesn't explicitly differentiate.

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

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

The description provides no guidance on when to use this tool versus alternatives such as index_daily or concept_members. The only note is the PRO requirement and 403 error, which is a prerequisite rather than usage guidance. No exclusions or alternative tool mentions are given.

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

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

With no annotations, the description carries the burden of behavioral disclosure. It does disclose the PRO plan requirement and the 403 response for lower tiers, which is important. It also lists all return fields, giving a clear picture of output. However, it omits other behavioral traits like rate limits, ordering, or error handling beyond 403.

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 compact, with a short header, a single requirement line, and structured Args/Returns lists. It front-loads the key access constraint (PRO) early. The duplication of schema descriptions is somewhat redundant but does not bloat the overall 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?

The description covers the PRO requirement, all parameters, and output fields, which is substantial. However, it does not explain how start_date/end_date relate to period/ann_date, leaving ambiguity for a tool with 5 optional parameters. The absence of an actual output schema is partially mitigated by the field list, but some usage semantics remain unclear.

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

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

Schema description coverage is 100%, and the description's Args section essentially repeats the schema text. No additional meaning is added beyond what the schema already provides. The return field list is useful but not parameter semantics, so the 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 opens with '财务指标（财务数据）' which clearly states the tool provides financial indicators. The Args and Returns sections further specify it retrieves metrics like EPS, ROE, and margins for a security. It does not explicitly name the action (e.g., 'retrieve'), but the resource is clear and distinct from 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?

No guidance is given on when to use this tool versus alternatives. The only usage-related note is the PRO requirement and 403 error, which is a constraint rather than a recommendation. Sibling tools like 'fundamentals' or 'technical_factors' are never mentioned.

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

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

With no annotations, the description carries the full burden for behavioral disclosure. It does state the return format (JSON array with specific fields), which is helpful. However, it does not clarify behavior when 'symbol' is null (the parameter is optional with default null), nor does it mention whether this is a read-only operation, error conditions, or authentication needs. The description is mostly a structural schema, lacking richer behavioral context.

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

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

The description is exceptionally concise and well-structured, with a clear title, Args section, and Returns section. Every sentence earns its place, and there is no fluff or redundant information. It is front-loaded with the tool's purpose and provides the essential parameter and return field list in a compact format.

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 (one optional parameter, no required fields), and the description covers the parameter and return fields adequately. However, the lack of an output schema in the structured data (though the description lists fields itself) and the missing behavior for null symbol leaves a completeness gap. The description is sufficient for a basic retrieval tool but not fully complete for an agent to predict all behaviors.

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 alone fully documents the 'symbol' parameter. The description does not add any meaning beyond the schema; it repeats the same example (000001.SZ). The parameter's optionality and the default-null behavior are present in the schema but their semantic implication (what happens when null) is undocumented. Baseline of 3 applies because the schema does the heavy lifting.

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

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

The description clearly identifies the tool as returning Shenwan industry classification (申万行业分类) for a stock symbol, with the resource being industry classification data. It distinguishes itself from sibling tools like 'concepts' or 'concept_members' by the specific domain (industry vs concept). The lack of an explicit verb like 'get' or 'retrieve' is minor because the Args/Returns structure implies a data retrieval operation.

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, nor does it mention any prerequisites or exclusions. With many sibling tools (stocks, concepts, basic), the absence of usage context is a clear gap. No mention of what to do when 'symbol' is omitted or how this relates to other classification 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?

With no annotations, the description carries full burden. It discloses that PRO or above is required and that lower tiers return HTTP 403, which is useful operational context. It also mentions the return format. However, it does not disclose other behaviors like whether results are paginated, rate limits, or what happens if no required parameters are provided (since all are optional).

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 and well-structured with labeled Args and Returns sections. Each line serves a purpose: explaining the tool type, subscription requirement, parameter meanings, mutual exclusivity, and return fields. There is no filler or redundant repetition beyond what's needed.

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 purpose, auth requirements, parameters, and return format, which is reasonably complete for a data-list tool. However, it does not explain what happens when no parameters are provided (all are optional), nor does it mention pagination or other typical listing behaviors. Given the absence of annotations, this leaves some ambiguity.

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 each parameter has a description in the schema. The description adds extra value by explicitly noting that trade_date is mutually exclusive with start_date/end_date, which is not indicated in the schema. It also clarifies the limit_type values (U/D/Z). This goes beyond 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 the tool provides a limit-up/down list (涨跌停榜单) and identifies it as market data (行情数据). This is a specific resource, and the name 'limit_list' reinforces the purpose. It distinguishes from siblings like 'daily' or 'top_list' by its focus on limit-up/down events, though it doesn't explicitly call out alternatives.

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

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

No guidance on when to use this tool versus sibling tools such as 'daily', 'realtime', or 'top_list'. The only usage-related info is the PRO subscription requirement and the note that trade_date is mutually exclusive with start/end dates, which is parameter behavior rather than tool selection 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 full burden. It does disclose the 403 error for insufficient subscription tiers and the exact return format (JSON array with fields). However, it does not mention read-only status explicitly (though implied), rate limits, or error handling for invalid inputs.

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 well-structured with distinct sections for requirement, arguments, and returns. It is relatively concise, though the Args section duplicates the schema descriptions, which is mildly redundant. The main purpose 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?

An output schema exists, so return-value details are not required. The description does not explain how the date parameters and period interact (e.g., whether period is an alternative to start/end_date). All parameters are optional, and no guidance on valid combinations is provided, leaving some ambiguity for agents.

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 full descriptions for all four parameters (100% coverage). The description's Args section repeats these descriptions verbatim, adding no new semantics such as valid combinations or parameter relationships. Baseline 3 is appropriate since the schema does the heavy lifting.

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

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

The description opens with '主营业务构成（财务数据）', which clearly identifies the resource as main business composition. Although it lacks an explicit verb like 'Get' or 'Retrieve', the Args and Returns sections make its data-retrieval purpose obvious, and it is distinct from sibling financial 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 mentions the premium requirement ('需要 PRO 及以上套餐（低档位调用返回 403）'), which is a prerequisite, but it does not provide guidance on when to use this tool versus alternatives like income or balance_sheet. No exclusions or preferred scenarios are given.

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

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

With no annotations provided, the description shoulders the transparency burden. It discloses the 403 error behavior for low-tier users and specifies the return format (JSON array with listed fields). It does not state explicitly that it is read-only, but '行情数据' implies a data retrieval 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?

The description is compact and front-loaded: purpose, access requirement, parameters, and returns are all covered in a concise block. No redundant text 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 simple data query tool, the description provides the essential context: what it does, access restriction, parameters, and output fields. The output schema exists, so return fields are redundant but harmless. It lacks only minor details like pagination or field definitions, but those are covered by the schema.

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

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

Schema description coverage is 100%, so baseline is 3. The description's parameter list merely repeats the schema's descriptions (symbol example, date format, and the trade_date mutual exclusivity). It adds no new 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 the tool provides '融资融券明细' (margin trading details) as market data. The verb '明细' and resource are specific and distinct from sibling tools, though it does not explicitly call out alternatives.

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

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

It mentions the PRO/PRO+ subscription requirement and that lower tiers return 403, which is a prerequisite/context. However, it does not explicitly state when to use this tool versus alternative data endpoints or provide exclusionary 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?

No annotations are provided, so the description carries the full burden. It discloses the PRO requirement, the specific 403 error on low-tier access, and the return format. This is strong for a read-only data retrieval tool, though it doesn't cover rate limits or data coverage nuances.

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 well-structured with Args and Returns sections, and front-loads the purpose. It is longer than minimal due to the detailed return field list, but every part serves a purpose. 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?

Given the tool's moderate complexity, the description covers the resource, permissions, error behavior, parameters, and output fields. The output schema exists, so the manual return list is extra but helpful. Minor gaps like maximum date range or pagination are acceptable here.

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%, but the description adds crucial semantics: it re-emphasizes the symbol pattern with an example and explicitly states the mutual exclusion between trade_date and start_date/end_date, which the schema does not convey. This goes well beyond 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 '个股资金流向' (individual stock money flow), which identifies the resource and distinguishes it from sibling tools like moneyflow_hsgt. However, it lacks an explicit verb like 'get' or 'query', relying on the tool name to imply the action.

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

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

It provides important context: PRO subscription required and low-tier returns 403, plus the mutually exclusive trade_date vs date range parameters. But it does not explicitly state when to use this tool over alternatives (e.g., moneyflow_hsgt) or 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?

With no annotations, the description carries the full burden. It discloses the return format (JSON array with specific fields), the error condition for insufficient plan level, and parameter constraints. This provides substantial transparency for a read-only data retrieval tool, though it could mention rate limits or pagination.

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 well-structured with required plan, args, and returns, but the Args block duplicates the schema's property descriptions, making it redundant. A more concise version could integrate parameter details more 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?

The description covers the key context: the whole-market scope, the PRO requirement, the date parameter modes, and a clear return schema with field names. For a tool of this complexity, it is sufficient, though it does not explain the meaning of each returned field (e.g., ggt_ss vs hgt_ss) which might be helpful.

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

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

Schema description coverage is 100%, and the Args section in the description largely repeats the schema's descriptions. The only added value is the explicit '与 start/end 二选一' mutual exclusion, which is already present in the schema. No meaningful new semantics beyond the structured data.

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 '沪深港通资金流向（全市场）' (whole-market Stock Connect capital flow), providing a specific resource and action. It distinguishes itself from sibling tools like 'moneyflow' by emphasizing '全市场' (whole market).

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

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

Mentions PRO+ requirement and the 403 error for lower tiers, which is useful context. Also notes the mutual exclusion of trade_date with start/end. However, it does not explicitly discuss when to use this tool versus sibling tools like moneyflow, northbound_holdings, or southbound_holdings, leaving comparison to the agent.

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 burden of behavioral disclosure. It reveals the access restriction (PRO required, 403 otherwise), the matching behavior (exact symbol match), and the return format (JSON array with specific fields). However, it does not disclose pagination, data freshness, or rate limits, which are common behavioral aspects for data APIs.

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 well-structured with clear Args and Returns sections, and the access requirement is front-loaded. It is concise and free of irrelevant details. A slight deduction because the parameter descriptions duplicate the schema, but overall it's efficient and organized.

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 there is no output schema provided, the description includes return fields, which is helpful. It covers the access restriction and parameter meanings. However, it lacks usage context relative to sibling tools and does not mention potential error cases (beyond the 403) or edge cases like empty results. For a simple data retrieval tool, it is adequate but not fully 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 description coverage is 100%, with each parameter already described (e.g., symbol, start_date, end_date, ann_date). The description's Args section largely duplicates these schema details, adding no new meaning beyond what the schema already provides. It does not clarify optionality or interplay between parameters, so the 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 indicates the tool returns ETF net asset value data ('ETF 净值'), and the return fields (unit_nav, accum_nav, etc.) reinforce this. However, it does not explicitly differentiate this from sibling tools like etf_daily or etf_adj_factor, so it misses the sibling distinction that would elevate it to a 5.

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 the PRO requirement and 403 error for lower tiers, which is a prerequisite, but it provides no guidance on when to use this tool versus alternatives like etf_daily or index_daily. There is no 'when to use' or 'when not to use' advice, so usage guidance is effectively absent beyond the access restriction.

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

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

With no annotations, the description carries the full burden and does well: it discloses the ~60-second delay, descending order by publish time, PRO requirement (403 on lower tiers), and the JSON array return structure. It could add more about rate limits or pagination, but the disclosed traits are valuable.

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 organized with Args and Returns sections and includes essential behavioral context, but the opening phrase '财经快讯（实时）（实时与快讯）' is redundant and cluttered, making it less concise than it could be. Still, most content earns its place.

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

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

The description covers the essential context: purpose, sources, required and optional parameters, access control, latency, and return fields. Given that an output schema exists and the tool is relatively simple, it is fairly complete, though it could mention pagination or result limits.

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

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

Schema description coverage is 100%, and the description's Args section essentially restates the schema's parameter descriptions without adding new meaning. It doesn't clarify anything beyond what the schema already provides, so the baseline 3 is appropriate.

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

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

The description clearly identifies the tool as providing real-time financial news flashes from three specific sources (cls, jin10, sina), which distinguishes it from other data tools. However, it lacks an explicit verb like 'retrieve' or 'list', and doesn't name competing sibling tools such as 'express'.

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 when to use the tool (to get real-time financial news from specified sources) and provides access constraints (PRO required, 403 otherwise), but it doesn't explicitly state when not to use it or mention alternatives. The usage guidance is implied rather than explicit.

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

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

With no annotations, the description carries the full burden. It discloses the low-frequency data cadence, retention of historical daily data, and the PRO plan requirement (lower tiers return 403). This adds significant behavioral context beyond the schema. It does not cover error handling beyond 403, but the disclosed traits are substantial.

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 compact and well-structured with 'Args' and 'Returns' sections. Every sentence is essential, covering cadence, access requirements, and return fields. No fluff or redundancy. The front-loaded title phrase immediately conveys the resource.

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

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

Given the tool's complexity, the description covers data frequency, auth constraints, parameters, and return fields. It does not explicitly state default behavior when no dates are passed, but the query suggestion implies that is acceptable. The presence of an output schema further reduces the need to explain return values. Overall, fairly 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 the baseline is 3. The description repeats parameter names and adds no new meaning beyond the schema. The note that trade_date is exclusive with start/end is already present in the schema description. No extra semantics provided.

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

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

The description opens with '北向持股明细（资金流向）' which clearly identifies the resource as northbound holdings details, distinguishing it from the sibling 'southbound_holdings'. Though it lacks an explicit verb like 'get' or 'query', the 'Returns' section confirms it provides data. The purpose is unambiguous.

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

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

The description provides clear context on when to use this tool, explaining the disclosure rhythm change (no longer daily since Aug 2024) and giving a query suggestion: '查询建议不带日期或用较宽日期范围'. It does not explicitly name alternatives, but the context makes the use case clear. Lacks explicit 'when not to use' 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 present, so the description must convey behavioral traits. It does mention the PRO requirement and indicates that symbols are matched exactly ('代码原样匹配'). It also specifies date formats and returns a JSON array with listed fields. However, it does not disclose whether at least one parameter is required, how parameters interact, or any pagination/rate limits, leaving gaps in the behavioral picture.

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 compact and well-structured, with a clear title, a brief note on subscription requirements, and separate Args and Returns sections. It avoids unnecessary detail and is easy to scan. The Args section repeats schema descriptions but does so succinctly, so the structure earns high marks.

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 an output schema and 5 optional parameters. The description covers parameter formats and return fields, but does not explain whether a symbol is effectively required, how date ranges are interpreted, or what the numeric fields (mkv, amount, etc.) represent. It also offers no comparison with sibling tools, so the context is adequate but not complete for a fully informed choice.

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

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

The input schema has 100% description coverage; every parameter includes a Chinese description that exactly mirrors the Args section in the tool description. The description adds no extra meaning beyond repeating the schema information, so the theoretical baseline of 3 applies. It includes one example (000001.SZ) which slightly aids understanding, but overall no new semantic value is added.

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 'ETF 持仓明细' (ETF holdings details), specifying the resource and the type of data returned. The purpose is unambiguous and the parameter list supports it. However, it does not explicitly distinguish from sibling tools like tracking_indices or etf_daily, so it is not a perfect 5.

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

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

The only usage guidance provided is the PRO subscription requirement and the 403 error for lower tiers. There is no information on when to prefer this tool over sibling alternatives, no exclusions, and no context on typical use cases. The agent is left to infer when 'portfolio' is appropriate.

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

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

With no annotations, the description carries a heavy burden, and it does well by disclosing the subscription requirement (PRO+, 403 otherwise) and the temporal snapshot behavior. It does not mention pagination or rate limits, but for a simple snapshot tool the disclosed context is substantive.

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 well-structured with a title line, a behavior paragraph, and Args/Returns sections. It is efficient and front-loaded with the core purpose. Minor redundancy: the first line '实时行情快照' is restated, and the parenthetical '（实时与快讯）' adds little value, but overall it is not bloated.

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 optional parameter) and the presence of an output schema, the description covers the essential context: what it returns, when it updates, and the subscription gate. It does not describe ordering or pagination, but these are not critical for this snapshot tool. The description is sufficiently complete for an agent to select and invoke it correctly.

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

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

Schema description coverage is 100%, and the description's Args section repeats the schema's parameter description verbatim without adding new meaning. The optional comma-separated batch of up to 200 symbols is already in the schema, so the description adds no parameter semantics beyond the structured definition.

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 real-time market snapshots ('实时行情快照') for each security, with explicit behavior during trading and non-trading hours. This distinguishes it from siblings like 'daily' (historical daily bars) and 'express' (news flash) by focusing on the latest intraday tick-level snapshot.

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 gives clear temporal context: during trading hours it refreshes continuously, and outside trading hours it returns the previous close. It also notes the PRO subscription requirement and 403 error for lower tiers. However, it does not explicitly name alternative tools or state when NOT to use this tool, so it falls short of a 5.

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

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

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It discloses the PRO requirement and 403 error behavior, and notes the JSON array return format. However, it omits other behavioral aspects such as rate limits, pagination, data freshness, or what happens when no data matches the query.

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 well-structured with a title line, access requirement, argument list, and return description. It is slightly repetitive of the schema, but the information is front-loaded and easy to scan, making it efficient 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 5-parameter data retrieval tool with optional parameters and an output description, the description adequately covers purpose, access requirements, parameter formats, and return fields. It does not elaborate on edge cases or the interplay between dates, but overall it provides enough context for a typical query scenario.

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

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

Schema description coverage is 100%, so the baseline is 3. The description restates each parameter with the same descriptions as the schema, adding no additional semantic meaning beyond what the schema already provides. It clarifies the distinction between end_date and enddate, but that is also present in 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 identifies the tool as returning 股东户数 (shareholder count) market data, with a specific resource and context. It mentions the data type and return fields, but does not explicitly distinguish it from sibling tools such as holder_trade or share_size, though the '行情数据' qualifier helps set it apart.

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 includes a usage constraint by noting the PRO subscription requirement and the 403 error for lower tiers, but it does not explicitly state when to use this tool versus alternatives or provide exclusions. The intended usage is implied by the tool's purpose, but no direct guidance is given.

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

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

With no annotations, the description compensates by disclosing the PRO plan requirement and 403 behavior for lower tiers, plus the date parameter exclusivity. It could further clarify default behavior when no params are provided, but the disclosed constraints add real value beyond the schema.

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

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

The description is organized into title, requirement, args, and returns sections with no filler. It is efficient, though the title 'ETF 份额与规模（ETF）' is slightly redundant with the name and repeats ETF twice.

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 5-param query tool with an output schema, the description covers all parameters, the auth constraint, date exclusivity, and return fields. It remains vague on what happens when no parameters are supplied, but overall it's sufficiently complete for selection and invocation.

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's Args section directly mirrors the schema descriptions, adding no new semantic meaning for parameters. The Returns section lists output fields but doesn't enrich parameter understanding, 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 states the tool returns ETF share and size data (ETF 份额与规模), which clearly identifies the resource. It lacks an explicit verb like 'query' or 'get', and doesn't name sibling alternatives, but the fields and ETF scope distinguish it from sibling tools like etf_daily or nav.

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

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

It specifies a hard requirement (PRO or higher, otherwise 403) and explains the trade_date vs start_date/end_date mutual exclusion. It does not explicitly list alternatives or when-not-to-use, but provides clear contextual usage for selecting date parameters.

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 burden of behavioral disclosure. It discloses the T+1 delay, the PRO-tier requirement with 403 behavior, and the return format (JSON array with specific fields). This is substantial but could mention pagination or error handling for missing 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 concise and well-structured: purpose, constraints, args, and returns. It front-loads the core purpose in the first line and uses a clean format. Every sentence earns its place.

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

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

The description covers core context: T+1 lag, subscription requirement, mutual exclusivity of date parameters, and return fields. It also benefits from the presence of an output schema. It could be more explicit about what happens when neither trade_date nor start/end is provided, but overall it is complete for a data retrieval 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%, so the baseline is 3. The description repeats schema descriptions for symbol, start_date, end_date, and trade_date, and explicitly notes the trade_date vs. start/end mutual exclusivity, but does not add meaningful semantics 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 identifies the resource as southbound (Stock Connect) shareholding details with fund flow, which distinguishes it from northbound_holdings. However, it lacks an explicit verb like 'get' or 'list', relying on the noun phrase '持股明细' to imply retrieval.

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 important operational context (T+1 data lag, PRO subscription requirement, 403 for lower tiers) but does not explicitly state when to use this tool versus alternatives such as northbound_holdings or moneyflow_hsgt. Usage is implied rather than spelled out.

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 discloses that the return is a JSON array with specific fields, which is useful. However, it does not mention whether filters are combined (AND), if at least one filter is required, if results are paginated or limited, or that it is a read-only operation. The behavioral info beyond the schema is minimal.

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

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

The description is well-structured and concise: a short title, an Args list matching the schema, and a Returns section. It is front-loaded, easy to scan, and contains no unnecessary prose. The redundancy with the schema is acceptable and does not inflate length unnecessarily.

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 output schema covers return values, but the description lacks important contextual details for an AI agent. It does not state that all parameters are optional, how filters interact, or whether calling with no filters returns the entire universe of stocks (which could be a huge response). It also does not explain the distinction from sibling tools, leaving the agent without enough context to choose this tool confidently.

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

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

The input schema covers 100% of the parameters with descriptions, so the schema already documents parameter meaning. The description repeats the same parameter descriptions without adding extra semantics such as filter combination logic, default behavior, or value formatting beyond what is in the schema. 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 identifies the tool as a stock list providing basic information, with a specific title '股票列表（基础信息）' and a Returns section defining the output fields. It is distinct from sibling tools like daily or fundamentals, although it does not explicitly name alternatives or state the scope (e.g., 'List all stocks matching filters').

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 lists filter parameters but does not state whether this should be used for querying basic stock metadata, whether it is the default for lookup, or what scenarios are better suited for other sibling tools. There is no explicit when-to-use or 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, the description carries the burden. It discloses the 403 error for lower tiers and enumerates all output fields, which is helpful. However, it omits behavioral aspects like pagination, date range limits, data source, or empty-result behavior. Transparency is partial.

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 and clearly segmented into Args and Returns. The PRO requirement is front-loaded, and every sentence carries useful information. It is compact without being under-specified.

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?

An output schema exists, so the return field list is a bonus. However, the description does not clarify whether a symbol is mandatory or how many of the date parameters are truly needed. It also fails to relate this tool to its sibling technical_factors_pro. These gaps make it incomplete for an agent to invoke correctly.

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

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

The input schema already describes all four parameters with 100% coverage. The Args section repeats the same descriptions without adding new semantics. The only extra note—trade_date exclusivity—is also present in the schema. Therefore, the description adds little 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 opens with '技术面因子（行情数据）' which is more a category label than a clear function statement. It lacks an explicit verb like 'retrieve' or 'get', and does not distinguish itself from the sibling tool technical_factors_pro. However, the Returns section clarifies that it provides technical indicators for a security.

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

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

The description provides concrete usage constraints: the PRO plan requirement and the exclusivity between trade_date and start/end dates. Yet it gives no guidance on when to prefer this tool over technical_factors_pro, daily, or indicators, and does not clarify whether a symbol is required. Usage context is only partially addressed.

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 burden of behavioral disclosure. It discloses the permission requirement (403 on lower tiers), the return format (JSON array), the full list of fields, and the mutual exclusivity between trade_date and start/end. It does not mention pagination, rate limits, or behavior with no parameters, but for a data retrieval tool, it covers the key operational facts.

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 structured with clear sections: a one-line purpose, a permission note, Args, and Returns. It is front-loaded and free of fluff. The extremely long field list is necessary for a data tool with many indicators, but it does make the description lengthy. Overall, it is appropriately organized and not redundant.

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 complex data retrieval tool with hundreds of fields, the description provides the essential context: permissions, parameter formats, mutual exclusivity, and the full output field list. The output schema is present in the description itself. It lacks details like data frequency (implied daily by trade_date), error handling for invalid symbols, or date range limits, but these are not critical gaps given the richness of the rest of the description.

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 all four parameters already described in the input schema. The description's Args section repeats the same descriptions without adding new semantic information, except perhaps reinforcing the trade_date vs start/end exclusivity. Given the schema fully documents the parameters, the description adds marginal value beyond it.

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

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

The description clearly identifies the tool as a provider of technical factors (pro version) including adjusted prices and technical indicators, and labels it as market data. This distinguishes it from the sibling 'technical_factors' tool. However, it lacks an explicit action verb (e.g., 'get', 'retrieve'), relying on the noun phrase '技术面因子' to imply data retrieval.

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

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

The description states the prerequisite that Expert or higher tier is required and that lower tiers get a 403 error, which gives usage context. It also notes that trade_date is mutually exclusive with start/end dates. However, it does not explicitly compare this tool to siblings like 'technical_factors' or 'daily' or state when to prefer this over those alternatives. Usage is implied rather than directly guided.

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 appropriately discloses that lower-tier subscriptions result in HTTP 403, and it describes the return format. It does not cover rate limits or other edge behaviors, but for a read-only market data endpoint this is reasonable.

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 compact and well-organized: a title line, a package requirement, an Args block, and a Returns block. Every sentence contributes useful information with no 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 four-parameter data retrieval tool with no annotations and an output schema, the description provides all necessary context: data source, access restrictions, parameter formats, and return fields. It is fully self-contained.

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's parameter section largely repeats schema info, but it adds the important constraint that trade_date is mutually exclusive with start_date/end_date. It also lists output fields, adding context beyond the input 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 identifies the tool as providing Dragon-Tiger List (institutional seats) market data, which is a specific resource and distinguishes it from the sibling top_list (general top list). It lacks an explicit verb but the intent is unambiguous.

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

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

The description explicitly states the PRO package requirement and the mutual exclusion between trade_date and start_date/end_date, providing actionable usage constraints. It does not explicitly contrast with alternatives like top_list, but the institutional-seat qualifier implies a distinct use case.

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?

Since no annotations are provided, the description carries the full burden and does well: it discloses the PRO subscription requirement, the 403 error behavior, the mutual exclusivity of trade_date versus start/end_date, and the return format. It does not mention rate limits or pagination, but for a read-only data tool the disclosed traits are adequate.

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

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

The description is short and structured with an Args/Returns format, making it easy to scan. The opening phrase is a fragment rather than a full sentence, but every part conveys essential information: purpose, subscription requirement, parameters, and return fields.

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 provides return field names and parameter constraints, but it is ambiguous whether a symbol is required or if queries without a symbol are valid, since all parameters are optional in the schema. The tool's query modes are not fully specified, which could lead to incorrect invocation.

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 description's Args section exactly repeats the parameter descriptions already present in the input schema (100% coverage). The description adds no new meaning beyond what the schema provides, so the baseline score of 3 is appropriate.

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

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

The description clearly identifies the tool as providing Dragon-Tiger List (龙虎榜) individual stock market data, and the return fields clarify it is a data retrieval tool. However, it lacks an explicit verb like 'get' or 'list' and does not distinguish itself from sibling tools such as top_inst or limit_list.

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 gives no guidance on when to use this tool versus alternatives. It only mentions the PRO subscription requirement and a 403 error for lower tiers, which is a prerequisite, not usage guidance 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, the description carries the full burden. It discloses the PRO+ subscription requirement and the 403 response for lower tiers, which is useful. It also states the return format. However, it does not mention read-only nature, pagination, or other error behaviors, leaving gaps.

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

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

The description is well-structured with distinct sections for requirements, args, and returns. It is reasonably compact, though the Args block repeats schema descriptions, which is redundant but not overly verbose.

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 access requirements, all parameters, and return fields, which is fairly complete for a data retrieval tool. It does not explain date parameter interactions or field meanings like 'bp' or 'adj_circle', but output schema likely covers those. Given the output schema exists, this level of description is adequate.

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

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

Schema coverage is 100%, so the baseline is 3. The description duplicates the schema's parameter descriptions without adding examples, constraints, or interaction semantics among start_date, end_date, and pub_date. It adds no value 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 indicates it provides basic information on indices tracked by ETFs, distinguishing it from sibling tools like index_daily or index_weight. However, it uses a noun phrase rather than an explicit action verb like 'get' or 'list', which slightly reduces clarity.

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

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

No guidance is given about when to use this tool versus alternatives. The only note is the PRO+ subscription requirement and 403 error, which is an access restriction, not usage guidance. No exclusions or alternative tool recommendations 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 the burden. It discloses that SSE is the default exchange and specifies the return fields (exchange, cal_date, is_open, pretrade_date), adding some context. However, it omits other behavioral traits such as valid date ranges, error handling, or whether start/end are required together.

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 compact and structured, listing parameters and return fields. However, the Args section duplicates schema information, which is unnecessary but not overly verbose.

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 presence of an output schema and the simplicity of a calendar lookup, the description is mostly complete, but it lacks usage context and deeper behavioral notes. The default exchange note is helpful, yet the tool would benefit from clarifying parameter dependencies.

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 covers 100% of parameters, so the baseline is 3. The description's Args section merely repeats the schema's descriptions without adding new meaning, constraints, or examples.

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 identifies the tool as a trading calendar data source ('交易日历') with default SSE exchange and basic info, which clearly indicates it provides trading calendar data. It lacks an explicit verb like 'list' or 'query', but the resource is unambiguous and distinct from 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?

No guidance is provided about when to use this tool vs alternatives. It does not reference any sibling tools or conditions for use, leaving the agent to infer from the name alone.

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