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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes the data retrieved but doesn't mention behavioral traits like whether this is a read-only operation, requires authentication, has rate limits, returns real-time or historical data, or what happens if parameters are invalid. For a data retrieval tool with zero annotation coverage, this is a significant gap.

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

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

The description is a single, efficient sentence that clearly states the purpose and key details. It's front-loaded with the main action ('获取大宗交易数据') and includes essential specifics without unnecessary elaboration, making it appropriately sized for its function.

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

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

Given the tool's moderate complexity (3 parameters, no output schema, no annotations), the description is minimally adequate. It explains what data is retrieved but lacks context on behavioral aspects, usage scenarios, or output format. Without annotations or an output schema, more detail on what the tool returns would be helpful for completeness.

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

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

Schema description coverage is 100%, so the schema already documents all three parameters (code, end_date, start_date) with their descriptions and types. The description doesn't add any parameter-specific information beyond what's in the schema, such as explaining the relationship between parameters or additional constraints. Baseline 3 is appropriate when 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 states what the tool does: '获取大宗交易数据' (get block trade data) with specific details like '成交价格、成交量、买卖双方营业部等详细信息' (transaction price, volume, buyer/seller departments, etc.). It uses a specific verb ('获取') and resource ('大宗交易数据'), but doesn't explicitly differentiate from sibling tools like 'stock_data' or 'margin_trade' that might also provide financial data.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'stock_data' for general stock information or 'margin_trade' for margin trading data, nor does it specify use cases or exclusions for block trade data specifically.

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 full burden for behavioral disclosure. While it indicates this is a data retrieval operation ('获取'), it doesn't specify whether this requires authentication, has rate limits, returns paginated results, or provides real-time versus historical data. The description lacks crucial operational context for a tool with 5 parameters.

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

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

The description is a single, efficiently packed sentence that lists all major data categories. While dense, it avoids redundancy and gets straight to the point. The structure could be slightly improved by front-loading the core purpose more clearly, but overall it's appropriately 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?

For a tool with 5 parameters, no annotations, and no output schema, the description provides adequate purpose clarity but lacks operational guidance and behavioral context. It covers what data is available but not how to effectively use the tool or interpret results. The absence of output schema means the description should ideally provide more context about return formats.

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

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

With 100% schema description coverage, the input schema already comprehensively documents all 5 parameters including detailed enum values for data_type. The description adds no additional parameter semantics beyond what's in the schema, so it meets the baseline of 3 but doesn't provide extra value.

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

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

The description explicitly states '获取上市公司综合表现数据' (retrieve comprehensive performance data for listed companies) and provides a detailed list of specific data types including financial statements, management information, and shareholder data. It clearly distinguishes this tool from siblings like stock_data or index_data by focusing on comprehensive company performance metrics rather than market trading data.

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

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

The description provides no guidance on when to use this tool versus alternatives. While it mentions comprehensive data retrieval, it doesn't specify scenarios where this tool is preferred over sibling tools like company_performance_hk or company_performance_us, nor does it mention prerequisites or constraints for usage.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves data ('获取'), implying a read-only operation, but doesn't clarify permissions, rate limits, data freshness, or error handling. For a financial data tool with 6 parameters and no annotation coverage, this leaves significant behavioral gaps unaddressed.

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

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

The description is a single, efficient sentence that front-loads the core purpose. It avoids redundancy and wastes no words, though it could be slightly more structured by separating scope from data types. Every part of the sentence contributes value, making it appropriately concise 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?

Given the tool's moderate complexity (6 parameters, financial data), lack of annotations, and no output schema, the description is minimally adequate. It covers the what (financial data retrieval) and scope (Hong Kong companies), but misses behavioral context, usage guidelines, and output details. The schema handles parameters well, but the description doesn't fill other gaps sufficiently for a higher score.

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 mentions financial statement types (income, balance, cashflow) which align with the 'data_type' parameter, but adds no additional parameter semantics beyond what the schema provides. With 100% schema description coverage, the baseline is 3. The description doesn't compensate for any gaps because there are none in the schema, but it also doesn't enhance parameter understanding beyond the schema's thorough documentation.

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

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

The description clearly states the tool's purpose: '获取港股上市公司综合表现数据，包括利润表、资产负债表、现金流量表等财务报表数据' (Get comprehensive performance data for Hong Kong-listed companies, including income statements, balance sheets, cash flow statements, and other financial report data). It specifies the verb ('获取' - get), resource ('港股上市公司综合表现数据' - Hong Kong-listed company performance data), and scope (financial statements). However, it doesn't explicitly differentiate from sibling tools like 'company_performance' or 'company_performance_us', which appear to be similar tools for different markets.

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 doesn't mention sibling tools like 'company_performance' (likely for other markets) or 'company_performance_us', nor does it specify prerequisites, exclusions, or contextual usage scenarios. The agent must infer usage from the tool name and description alone.

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

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

No annotations are provided, so the description carries full burden. It implies a read-only operation by stating '获取' (get/retrieve), but doesn't disclose behavioral traits like authentication needs, rate limits, data freshness, error handling, or output format. For a tool with 5 parameters and no annotations, this is a significant gap in transparency.

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

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

The description is a single, efficient sentence that front-loads the core purpose. It wastes no words but could be slightly more structured by separating data types for clarity. Every part earns its place, though it lacks completeness for a tool with no annotations or output schema.

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

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

Given the complexity (5 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain the return values, data structure, or behavioral context needed for effective use. While the schema covers parameters well, the overall context for a financial data tool with multiple data types is insufficient.

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

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

Schema description coverage is 100%, providing detailed parameter documentation. The description adds no additional parameter semantics beyond what's in the schema, such as explaining interactions between parameters or data format examples. With high schema coverage, the baseline score of 3 is appropriate as 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 states the tool retrieves comprehensive performance data for US-listed companies, including income statements, balance sheets, cash flow statements, and financial indicators. It specifies the resource (US-listed companies) and data types, but doesn't explicitly differentiate from sibling tools like 'company_performance' or 'company_performance_hk' beyond the 'us' in the name.

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

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

No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, exclusions, or compare it to sibling tools like 'company_performance' or 'stock_data', leaving the agent to infer usage from the name and parameters alone.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. While it explains the two query modes, it doesn't describe what 'non-market data' specifically means, what format the data returns, whether there are rate limits, authentication requirements, or error conditions. For a financial data tool with zero annotation coverage, this leaves significant behavioral gaps.

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

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

The description is perfectly concise and front-loaded: one sentence stating the overall purpose followed by two clearly numbered query methods. Every sentence earns its place with no wasted words or redundant information.

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

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

Given the tool's moderate complexity (4 parameters, two distinct query modes) and 100% schema coverage but no output schema or annotations, the description is adequate but incomplete. It explains what the tool does and the two usage patterns, but doesn't address return values, error handling, or behavioral constraints that would be important for an AI agent to use it correctly.

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

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

The schema has 100% description coverage, so parameters are well-documented in the schema itself. The description adds value by explaining the two query modes that correspond to parameter combinations (issue type uses date parameters, info type uses ts_code), but doesn't provide additional semantic context beyond what's already in the schema 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 states the tool's purpose: '获取可转债非行情数据' (get convertible bond non-market data) with specific query methods. It distinguishes between two distinct use cases (issue data by date range and info data by code), making the purpose explicit and differentiated from sibling tools which appear to handle different financial data types.

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 for when to use each query type: '1)使用issue类型按时间范围查询可转债发行数据；2)使用info类型按代码查询可转债详细信息' (1) use issue type to query convertible bond issuance data by time range; 2) use info type to query convertible bond detailed information by code). However, it doesn't explicitly state when NOT to use this tool or mention alternatives among sibling tools.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. It lists what data is retrieved but doesn't mention rate limits, authentication requirements, data freshness, pagination, error conditions, or whether this is a read-only operation. For a data retrieval tool with 3 parameters, this leaves significant behavioral gaps.

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

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

The description is a single, dense sentence that efficiently lists all data types retrieved. While comprehensive, it could benefit from structural separation of the different data categories. However, every element serves a purpose and there's no wasted text.

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

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

For a data retrieval tool with 3 parameters and no output schema, the description provides good coverage of what data is returned but lacks information about return format, structure, or limitations. Without annotations or output schema, the agent has incomplete context about how to interpret results.

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%, providing good documentation for all 3 parameters. The description doesn't add parameter-specific information beyond what's in the schema, but with complete schema coverage, the baseline score of 3 is appropriate as the schema handles the parameter documentation adequately.

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

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

The description clearly states the tool retrieves data for CSI indices including market performance, constituent weights, and valuation/financial metrics. It specifies the resource (CSI indices) and verb (获取/retrieve), but doesn't explicitly differentiate from sibling tools like 'index_data' which might have overlapping functionality.

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 versus alternatives. The description doesn't mention sibling tools like 'index_data' or specify use cases, prerequisites, or exclusions. The agent must infer usage from the description alone.

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

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

No annotations are provided, so the description carries full burden. While it mentions the timezone (East 8th/China) and what information is included, it doesn't disclose important behavioral aspects like whether this is a read-only operation, if it requires authentication, rate limits, error conditions, or what format the response takes. For a tool with no annotation coverage, this leaves significant gaps in understanding its 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 a single, efficient Chinese sentence that communicates the core purpose without any wasted words. It's appropriately sized for a simple utility tool and front-loads the essential information about what the tool does and what timezone it uses.

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 timestamp utility with one optional parameter and no output schema, the description is moderately complete. It covers the core purpose and timezone context but lacks behavioral details that would be helpful given the absence of annotations. The description doesn't need to explain return values since there's no output schema, but it could benefit from mentioning the response format or typical use cases.

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

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

The description doesn't mention the 'format' parameter at all, while the input schema has 100% description coverage with clear documentation of the format options. Since schema_description_coverage is high (>80%), the baseline is 3 even without parameter information in the description. The description adds no value beyond what the schema already provides about parameters.

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

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

The description clearly states the tool's purpose with specific verb ('获取' - get/retrieve) and resource ('当前东八区（中国时区）的时间戳' - current East 8th zone/China timezone timestamp), including what information it provides ('年月日时分秒信息' - year, month, day, hour, minute, second information). It distinguishes itself from sibling tools which appear to be financial data tools, making this a specialized utility function.

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. While the purpose is clear, there's no mention of use cases, prerequisites, or comparison to other time-related tools (though none appear in the sibling list). The agent must infer usage purely from the purpose statement.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the return format ('返回表格包含' - returns a table containing) but lacks details on pagination, rate limits, authentication needs, error handling, or data freshness. For a data retrieval tool with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves beyond basic input-output.

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 front-loaded, stating the purpose and key parameters in a single sentence. Every element (purpose, required/optional parameters, return content) earns its place without redundancy. However, the lack of structural formatting (e.g., bullet points) and slightly dense phrasing in Chinese might reduce immediate clarity for non-native readers.

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 (2 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and parameters but lacks details on output structure (beyond mentioning table content), error cases, or usage scenarios. Without annotations or output schema, more context on behavioral aspects would improve completeness for effective agent use.

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

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

Schema description coverage is 100%, with clear descriptions for both parameters in the input schema. The description adds minimal value beyond the schema by reiterating that 'trade_date' is required and 'ts_code' is optional, but doesn't provide additional context like example usage patterns or implications of omitting the optional parameter. Baseline score of 3 is appropriate as 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 states the tool retrieves '龙虎榜机构成交明细（top_inst）' (dragon-tiger list institutional transaction details), specifying it returns a table with buy/sell/net amounts and listing reasons. It uses specific verbs ('返回表格包含' - returns a table containing) and identifies the resource, but doesn't explicitly differentiate from sibling tools like 'block_trade' or 'money_flow' which might have overlapping financial data domains.

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 by specifying required parameters ('必填：交易日期' - required: trade date) and optional ones ('可选：股票TS代码' - optional: stock TS code), which provides basic context for when to provide inputs. However, it doesn't offer explicit guidance on when to use this tool versus alternatives like 'stock_data' or 'money_flow', nor does it mention any exclusions or prerequisites beyond parameter requirements.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. While it mentions '通过真正的搜索API' (through a real search API) and '智能搜索' (intelligent search), it doesn't disclose important behavioral traits like whether this is a read-only operation, potential rate limits, authentication requirements, what '主流财经媒体' (mainstream financial media) specifically includes, or how results are returned. The description is insufficient for a tool with no annotation coverage.

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 appropriately concise with a single sentence that efficiently communicates the core functionality. It's front-loaded with the main purpose and doesn't contain unnecessary information, though it could be slightly more structured by separating the purpose from the parameter guidance.

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

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

Given the tool has no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns (news articles, headlines, summaries?), the format of results, potential limitations, or error conditions. For a search tool that presumably returns complex news data, this level of description is inadequate.

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

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

The input schema has 100% description coverage, with the query parameter fully documented in the schema itself. The description adds minimal value beyond the schema, only repeating that it supports '单个或多个关键词智能搜索' (single or multiple keyword intelligent search) without providing additional semantic context about how the search works or what '智能' (intelligent) specifically means.

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

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

The description clearly states the tool's purpose: '获取主流财经媒体的新闻内容' (get financial news content from mainstream media) and '支持单个或多个关键词智能搜索' (supports single or multiple keyword intelligent search). It specifies the verb (获取/get) and resource (新闻内容/news content), but doesn't explicitly differentiate from sibling tools like 'macro_econ' or 'stock_data' which might also provide financial information.

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 doesn't mention when this tool is appropriate compared to sibling tools like 'company_performance', 'macro_econ', or 'stock_data', nor does it provide any context about when not to use it or what prerequisites might be needed.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. While the description mentions what data can be obtained, it doesn't disclose important behavioral traits like whether this is a read-only operation, potential data volume issues (though hinted in schema), rate limits, authentication requirements, or error conditions. The description is purely functional without behavioral context.

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

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

The description is a single, efficient Chinese sentence that clearly states the tool's purpose and lists available data types. It's appropriately sized and front-loaded with the main purpose. There's no wasted verbiage, though it could potentially be structured better for readability with bullet points or clearer organization.

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

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

Given the tool has 5 parameters, no annotations, and no output schema, the description is somewhat incomplete. While it states what data can be obtained, it doesn't address important contextual aspects like response format, data volume considerations (though hinted in schema), error handling, or performance characteristics. For a data retrieval tool with multiple parameters and no output schema, more completeness would be helpful.

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

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

With 100% schema description coverage, the schema already documents all 5 parameters thoroughly. The description doesn't add any parameter semantics beyond what's in the schema - it doesn't explain parameter relationships, provide examples of parameter combinations, or clarify edge cases. The baseline of 3 is appropriate when 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 states the tool's purpose: '获取公募基金全面数据' (get comprehensive public fund data) and lists specific data types including fund lists, fund managers, net asset values, dividends, and holdings. It provides a specific verb ('获取' - get) and resource ('公募基金全面数据' - comprehensive public fund data), but doesn't explicitly distinguish this tool from sibling tools like 'fund_manager_by_name' which appears to be a more specialized tool.

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 context by listing the data types available, but doesn't provide explicit guidance on when to use this tool versus alternatives like 'fund_manager_by_name'. There's no mention of when not to use this tool or clear comparison with sibling tools. The usage context is somewhat implied through the data_type parameter options.

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

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

No annotations are provided, so the description carries full burden. It describes a read-only query operation but doesn't disclose behavioral traits like rate limits, authentication needs, data freshness, error conditions, or response format. For a tool with no annotation coverage, this leaves critical operational context unspecified.

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

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

The description is a single, efficient sentence in Chinese that front-loads the core purpose. It wastes no words but could be slightly more structured (e.g., separating key details). Every part earns its place, though it lacks explicit formatting for 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?

Given no annotations, no output schema, and a query tool with two parameters, the description is incomplete. It doesn't cover return values, error handling, or operational constraints. While the purpose is clear, the lack of behavioral and output information makes it inadequate for confident tool 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 description coverage is 100%, so the schema fully documents both parameters (name and ann_date). The description adds no parameter-specific semantics beyond what's in the schema—it doesn't explain how parameters interact or provide usage examples. This meets the baseline for high schema coverage but doesn't add value.

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

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

The description clearly states the tool's purpose: querying fund manager details by name, with specific resources listed (managed funds, personal background, work experience). It distinguishes this from sibling tools like fund_data or stock_data by focusing on individual managers rather than fund or stock data. However, it doesn't explicitly differentiate from potential similar tools not present in the sibling 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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, constraints, or compare it to other tools for fund manager information. With sibling tools like fund_data potentially containing overlapping data, the lack of usage context is a significant gap.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the source ('Tushare新闻接口') and implies real-time/latest news ('最新的'), but lacks critical details: it doesn't specify authentication needs, rate limits, error handling, or what '7x24' operationally means. The schema description mentions content deduplication, but this isn't highlighted in the main description. For a news-fetching tool with zero annotation coverage, this is insufficient.

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

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

The description is a single, efficient sentence that front-loads the core purpose. It wastes no words but could be slightly more structured (e.g., separating source from categories). However, it appropriately conveys the essential 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?

Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., news format, fields, timestamps) or behavioral aspects like pagination, rate limits, or error conditions. For a news-fetching tool that likely returns structured data, this leaves significant gaps for an AI agent to understand how to interpret results.

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 adds no parameter information beyond what the schema provides. The input schema has 100% description coverage for the single parameter 'limit', detailing its purpose, default, range, and deduplication behavior. With high schema coverage and only one parameter, the baseline score of 3 is appropriate - the description doesn't compensate but doesn't need to given the schema's completeness.

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

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

The description clearly states the tool's purpose: '从Tushare新闻接口获取最新的财经、政治、科技、体育、娱乐、军事、社会、国际等新闻' (fetch latest news from Tushare news API across multiple categories). It specifies the verb ('获取' - fetch), resource ('新闻' - news), and source ('Tushare新闻接口'). However, it doesn't explicitly differentiate from sibling tools like 'finance_news' - both appear to fetch news, though 'hot_news_7x24' emphasizes 'latest' and '7x24' (24/7) while 'finance_news' might be finance-specific.

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 doesn't mention sibling tools like 'finance_news' or explain scenarios where this tool is preferred (e.g., for broader news categories vs. finance-only). There's also no information about prerequisites, timing considerations, 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 full burden of behavioral disclosure. It states the tool retrieves data but doesn't describe what kind of data (e.g., historical prices, volumes), the format of the response, potential rate limits, authentication needs, or error conditions. For a data retrieval tool with zero annotation coverage, this is a significant gap.

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

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

The description is a single, efficient sentence that directly states the tool's purpose with relevant examples. It's front-loaded with the core functionality and wastes no words. Every part of the sentence earns its place by clarifying the scope.

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

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

Given the complexity of financial data retrieval, no annotations, and no output schema, the description is incomplete. It doesn't explain what data is returned (e.g., OHLC prices, volumes), the response format, or any behavioral aspects like pagination or errors. For a tool with three parameters and no structured output documentation, more context is needed.

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

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

Schema description coverage is 100%, so the schema already documents all three parameters (code, start_date, end_date) with clear descriptions and examples. The description adds no additional parameter semantics beyond what's in the schema, such as date range constraints or code validation rules. Baseline 3 is appropriate when 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 states the tool's purpose: '获取指定股票指数的数据' (get data for specified stock indices). It specifies the resource (stock indices) and provides examples (上证指数, 深证成指). However, it doesn't explicitly distinguish this tool from sibling tools like 'stock_data' or 'fund_data', which might handle similar financial data but for different asset classes.

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 doesn't mention sibling tools like 'stock_data' (for individual stocks) or 'fund_data' (for funds), nor does it specify prerequisites or exclusions. The agent must infer usage from the tool name and description alone.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. While it lists data types, it doesn't describe critical behaviors: whether this is a read-only operation, what data sources are used, historical coverage limits, rate limits, authentication requirements, or error conditions. For a data retrieval tool with 3 parameters, this leaves significant behavioral gaps.

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

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

The description is a single, efficient sentence that lists all available indicators. While dense, every element earns its place by specifying the data scope. It could be improved with front-loaded categorization (e.g., 'Retrieves macroeconomic indicators including...'), but it avoids redundancy and stays focused.

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 3-parameter data retrieval tool with no annotations and no output schema, the description is minimally adequate. It covers what data is available but lacks critical context: output format, data freshness, source reliability, error handling, and usage constraints. The 100% schema coverage helps, but behavioral and output information gaps remain significant.

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 clear parameter descriptions in the schema. The description adds value by listing all possible indicator values (shibor, lpr, gdp, etc.), which provides semantic context beyond the schema's '指标类型' description. However, it doesn't explain parameter interactions or constraints beyond what's already documented 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 states the tool's purpose as '获取宏观经济数据' (get macroeconomic data) and provides a comprehensive list of specific indicators including Shibor, LPR, GDP, CPI, etc. This is a clear verb+resource combination, though it doesn't explicitly differentiate from sibling tools like 'index_data' or 'stock_data' which might also provide economic indicators.

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

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

The description provides no guidance on when to use this tool versus alternatives. There's no mention of prerequisites, limitations, or comparison with sibling tools like 'index_data' or 'company_performance' that might overlap in economic data coverage. The user must infer usage from the indicator list alone.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool '获取' (gets/retrieves) data, implying a read-only operation, but doesn't clarify if it's real-time or historical, requires authentication, has rate limits, or returns paginated results. For a data retrieval tool with 5 parameters and no annotation coverage, this is a significant gap in transparency.

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

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

The description is a single, efficient sentence that front-loads the core purpose ('获取融资融券相关数据') and enumerates data types. There's no wasted text, but it could be slightly more structured (e.g., bullet points for data types). It earns its place by clarifying scope, though it lacks depth in usage or behavior.

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 (5 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain return values, error conditions, or data freshness. While the schema covers parameters well, the description fails to compensate for missing behavioral context, making it inadequate for a multi-parameter data retrieval tool without structured support.

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

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

Schema description coverage is 100%, so the schema already documents all parameters thoroughly (e.g., data_type options, date formats, exchange codes). The description adds minimal value by listing data types in Chinese, but doesn't explain parameter interactions (e.g., ts_code is optional for some data_types) or provide examples beyond what's in the schema. Baseline 3 is appropriate when 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 states the tool's purpose: '获取融资融券相关数据' (get margin trading related data) and lists specific data types like margin_secs, margin, margin_detail, and slb_len_mm. It distinguishes itself from siblings by focusing on margin trading data, unlike tools for stock_data, index_data, or macro_econ. However, it doesn't explicitly differentiate from all siblings (e.g., money_flow could overlap in financial data).

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

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

The description provides no guidance on when to use this tool versus alternatives. It lists data types but doesn't explain which to choose for specific scenarios (e.g., margin_secs for eligible stocks vs. margin for summary data). No exclusions, prerequisites, or comparisons to sibling tools are mentioned, leaving the agent to infer usage from parameter descriptions alone.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the types of data retrieved but lacks critical behavioral details: it doesn't specify if this is a read-only operation (implied by '获取' but not explicit), any rate limits, authentication requirements, or how the data is returned (e.g., format, pagination). For a tool with 6 parameters and no annotations, this is a significant gap in transparency.

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

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

The description is a single, efficient sentence in Chinese that front-loads the core purpose and lists key data points without unnecessary details. It could be slightly more structured by separating use cases, but it avoids redundancy and wastes no words, making it appropriately concise 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?

Given the tool's complexity (6 parameters, no annotations, no output schema), the description is incomplete. It lacks information on behavioral traits (e.g., read-only status, error handling), output format, and practical usage guidelines. Without annotations or an output schema, the description should provide more context to help an agent invoke the tool correctly, but it falls short, leaving gaps in understanding how the tool behaves and what it returns.

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

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

The schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema—it doesn't explain parameter interactions, default behaviors (e.g., automatic judgment based on ts_code), or usage examples. Given the high schema coverage, a baseline score of 3 is appropriate as the description doesn't compensate but also doesn't detract.

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

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

The description clearly states the action ('获取' meaning 'get' or 'retrieve') and specifies the exact resource: '资金流向数据' (money flow data) for stocks, markets, and sectors. It distinguishes itself from siblings by focusing on money flow metrics like net inflow amounts and percentages across different order sizes (main force, ultra-large, large, medium, small), which is not covered by other tools like stock_data or index_data.

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

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

The description implies usage by listing the data types (stocks, markets, sectors) and metrics, but does not explicitly state when to use this tool versus alternatives. For example, it doesn't compare to stock_data (which might provide general stock info) or specify scenarios where money flow data is preferred over other financial metrics. This leaves some ambiguity in tool selection.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes what data is retrieved (historical market data) and supports technical indicators, but does not mention rate limits, authentication needs, data freshness, error handling, or response format. For a data retrieval tool with no annotation coverage, this leaves significant behavioral gaps.

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

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

The description is a single, efficient sentence that front-loads the core purpose ('获取指定股票/加密资产的历史行情数据') and then lists supported asset types and data sources. Every part earns its place by clarifying scope and sources without redundancy or fluff.

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

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

Given the tool's complexity (5 parameters, no output schema, no annotations), the description is moderately complete. It covers the purpose and asset scope well, but lacks details on behavioral aspects like rate limits or response structure, and does not fully compensate for the absence of annotations and output schema. It's adequate but has clear gaps 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?

The schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds value by specifying the asset types and data sources, which helps contextualize the 'market_type' and 'code' parameters, but does not provide additional syntax or format details beyond what the schema provides. With high schema coverage, the baseline is 3, but the description's contextual info justifies a 4.

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

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

The description clearly states the tool's purpose with specific verbs ('获取' meaning 'retrieve' or 'get') and resources ('历史行情数据' meaning 'historical market data'), and distinguishes it from siblings by specifying the asset types supported (A股, 美股, 港股, etc.) and data source for cryptocurrencies (CoinGecko). This is more specific than just 'stock data' and differentiates from tools like 'stock_data_minutes' or 'index_data'.

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

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

The description implies usage by listing supported asset types and data sources, but does not explicitly state when to use this tool versus alternatives like 'stock_data_minutes' (which likely provides minute-level data) or 'index_data' (for index data). It provides context on what assets are covered but lacks explicit guidance on tool selection among siblings.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. The description mentions the tool '支持' (supports) specific frequencies and requires start/end datetime, but doesn't disclose critical behavioral traits like whether this is a read-only operation, potential rate limits, authentication needs, data freshness, error handling, or what the output looks like. For a data retrieval tool with no annotations, this leaves significant gaps.

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

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

The description is concise and front-loaded, stating the core purpose in the first phrase. The single sentence efficiently covers markets, frequencies, and time range requirement. However, it could be slightly more structured by separating market support from frequency options for 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?

Given the complexity (5 required parameters, no annotations, no output schema), the description is incomplete. It adequately states what the tool does but lacks crucial context: no output format information, no behavioral details (rate limits, errors), no differentiation from siblings, and minimal parameter guidance beyond schema repetition. For a data retrieval tool with multiple parameters, this leaves the agent under-informed.

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 adds minimal value beyond the input schema. It mentions the supported frequencies (1MIN/5MIN/15MIN/30MIN/60MIN) and that time range is required, but the schema already has 100% coverage with detailed descriptions for all 5 parameters. The description doesn't provide additional context like parameter interactions, constraints, or examples beyond what's 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 states the tool's purpose: '获取分钟K线数据' (get minute K-line data) for A股/加密 (A-shares/crypto). It specifies the resource (K-line data) and scope (minute-level, specific markets). However, it doesn't explicitly distinguish this from sibling tools like 'stock_data' or 'index_data', which might also provide stock-related data.

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

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

The description provides no guidance on when to use this tool versus alternatives. It mentions supported markets (A股/加密) and frequency options, but doesn't explain when this tool is appropriate compared to other stock data tools in the sibling list, such as 'stock_data' (which might provide different granularity) or 'index_data' (which might provide index-level data).

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