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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds the source URL and return type, but no additional behavioral traits such as data freshness, rate limits, or dependencies. Given the annotations, a score of 3 is appropriate—it adds some value but not rich 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: a single purpose line, a source URL, and return type information. It is front-loaded, contains no fluff, and every element serves a purpose.

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

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

With no output schema, the description gives only a high-level return type ('城市映射' as pandas.DataFrame). It does not detail the structure of the mapping (e.g., city names/IDs) or explain how this tool supports sibling air quality tools. For a simple no-param read-only tool, it is adequate but leaves gaps in contextual linkage.

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

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

The tool has zero parameters, so the schema coverage is trivially 100% and there is nothing to explain. The description clarifies the return as a city mapping DataFrame, which is useful. Baseline 4 applies for zero-param tools.

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

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

The description clearly states '真气网-空气质量历史数据查询-全部城市列表' (Air quality historical data query - all city list) and specifies the return as a city mapping (城市映射) in a pandas DataFrame. This distinguishes it from sibling tools like air_quality_hist and air_quality_rank, which handle historical data and rankings 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 provides no guidance on when to use this tool vs. alternatives. It does not mention that this tool provides the city list needed for other air quality tools (e.g., to get city codes before querying historical data). The usage context is entirely absent.

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?

Annotations already declare readOnly, openWorld, idempotent and non-destructive, so safety is covered. The description adds the data source URL, the 6-day forecast horizon, and the pandas.DataFrame return type, which is useful context, but it does not explain freshness, update cadence, or coverage limits.

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 content is compact but structured as raw source docstring fragments (URL, ':return:', ':rtype:') in mixed Chinese/English, and the return-type metadata is not front-loaded. It reads as pasted documentation rather than a crafted description.

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

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

For a no-argument read-only forecast tool with no output schema, the description conveys scope (6 days, Hebei) and return type, which is roughly adequate. The unresolved city-parameter mismatch is the main completeness gap.

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 declares zero parameters, yet the description documents a 'city' argument (city="" returns all regions; city="唐山市" returns Tangshan). That meaning is helpful in isolation but contradicts the empty input schema, leaving the agent unsure whether city can actually be passed.

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?

States a specific verb+resource: Hebei Province air quality forecast for the next 6 days, with the issuing system and URL. It is clearly distinguishable in subject matter from the finance/macro siblings, though it does not explicitly name any sibling it maps against.

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 when-to-use, when-not-to-use, or alternative guidance is given. The only usage hint is the city-filter behavior, and that is presented as part of the return note rather than as routing 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?

Annotations already declare readOnly/idempotent/openWorld/destructive=false, so the safety profile is covered. The description adds a genuine behavioral trait beyond annotations — hourly granularity is slow to download — plus the city-list dependency, but says nothing about rate limits or response shape beyond rtype.

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?

Front-loaded with purpose and source, then organized :param blocks with no filler. Slightly heavier than needed due to the URL and :type/:rtype scaffolding, but every line carries information (dependencies, format, tradeoffs).

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 4-parameter tool with 0% schema coverage and no output schema, the description covers every parameter, the return type (pandas.DataFrame), the city-list dependency, and the period tradeoff. An agent has enough to call it correctly; only error/edge behavior is unaddressed.

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 0%, so the description must carry the load, and it does: it documents all four parameters, explains the period values ('hour'/'day'/'month') with their data-volume tradeoff, gives date format examples ("20190327"), and points to air_city_table for valid cities. Only the exact city-value format is left implicit.

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 names the source (真气网/zq12369.com) and states it returns historical air quality data for a city/period range, which is a specific verb+resource. It distinguishes itself from realtime siblings like air_quality_watch_point by the '历史数据' framing, though it never names an alternative explicitly.

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 gives a prerequisite hint (call ak.air_city_table() to get the city list) and warns that 'hour' downloads are slow due to large data volume, which is useful guidance. However there is no explicit when-to-use / when-not-to-use versus the many air_quality_* siblings, so usage 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?

Annotations already declare read-only, idempotent, open-world, non-destructive behavior, so the safety profile is covered. The description adds a return type (pandas.DataFrame) and the source URL, but it does not disclose rate limits, authentication needs, data freshness, or sorting behavior beyond what the URL implies.

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 front-loaded with the resource name and then gives parameter and return details efficiently. The long raw URL with query parameters is somewhat bulky, but it provides a concrete source and example. Overall it is reasonably concise for a one-parameter tool.

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

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

For a simple one-parameter retrieval tool with no output schema, the description supplies the source, parameter value mappings, and return type. It omits return columns, sorting order, and data recency, but it is otherwise complete enough for an agent to 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 0%, so the description carries the full burden. It does so well by mapping four concrete date value patterns (empty, 20200312, 202003, 2019) to their meanings: current, daily, monthly, and yearly AQI rankings. This adds substantial meaning beyond the bare 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?

States a specific resource: a 168-city AQI ranking from 真气网. The purpose is clear for an agent, but the description does not explicitly differentiate from siblings such as air_quality_watch_point or air_quality_hist, leaving the distinction to be inferred from the tool name and resource scope.

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 explains what different date strings retrieve, but it gives no guidance on when to use this tool versus sibling air-quality tools or any other alternatives. There are no exclusions, prerequisites, or contextual triggers for 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?

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds limited extra transparency by specifying the data source URL and the return type (pandas.DataFrame). It does not describe edge cases, potential failures, or detailed output structure, but given the annotations cover safety, the added context 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 poorly structured: it begins with a redundant title line, includes a broken sentence ('指定之间段之间的空气质量数据'), and mixes Chinese and English unevenly. The docstring-style parameter sections are organized, but the front matter contains unnecessary repetition and awkward phrasing, making it less concise and clear than it could be.

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, return type, and source URL. However, without an output schema, it gives only a high-level description of the return ('观测点空气质量') and does not specify columns or metrics. For a simple retrieval tool with three string parameters, this is acceptable but not comprehensive.

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

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

With 0% schema description coverage, the description fully compensates by providing parameter types, format examples for dates, and a lookup method for city values. This is highly valuable for an agent to construct valid calls. The only minor gap is not explicitly stating the date format beyond 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 clearly states the tool's function: retrieving air quality data for each monitoring point in a specific city within a date range. The phrase '细化到具体城市的每个监测点' highlights the per-station granularity, which distinguishes it from sibling tools like air_quality_hist or air_quality_rank. However, it lacks an explicit verb like 'get' or 'fetch', and the phrasing is somewhat awkward.

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

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

The usage context is implied through the description of per-station data, and the parameter doc for 'city' explicitly instructs users to call ak.air_city_table() to obtain valid city values. No explicit alternatives or exclusions are provided, making it unclear when to choose this tool over sibling air quality 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?

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds the source URL and return type (pandas.DataFrame), which is useful but does not disclose any additional behavioral traits like pagination or data 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 compact, front-loaded with the exact data source, and includes only necessary details: source, URL, and return type. Every line 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?

For a simple no-parameter tool with strong annotations, the description is mostly complete. It identifies the source and return type, though it does not mention what columns or rows are included in the DataFrame, leaving minor 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?

The tool has zero parameters, so the baseline is 4. The description reinforces the return type (pandas.DataFrame) and the specific data content, which is sufficient for a parameterless tool.

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 AMAC's information disclosure for securities company direct investment funds, and states it returns a pandas.DataFrame. However, it lacks an explicit verb like 'retrieve' or 'list', 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?

There is no guidance on when to use this tool versus the many sibling AMAC tools (e.g., amac_fund_info, amac_fund_sub_info). The description only states what it returns, not when to choose it over alternatives.

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

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

The annotations already declare readOnlyHint, idempotentHint, and destructiveHint, which cover the safety profile. The description adds the source URL and return type, but no further behavioral traits such as pagination, rate limits, or authentication needs. No contradiction with annotations.

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 front-loaded with the core information: the source, the endpoint URL, and return type. However, the Chinese title is repeated verbatim in the first line and then again in the 'return' line, making it slightly redundant though not 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?

For a parameterless tool with rich annotations, the description is adequately complete: it names the exact data (ABS plan disclosure), provides the source URL, and states the return type (pandas.DataFrame). However, it does not describe the content of the DataFrame beyond the general 'disclosure information' label, so an agent might not know exactly what columns or coverage to expect.

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

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

The tool has zero parameters, so there is nothing for the description to explain. The baseline for 0-parameter tools is 4, and the description appropriately includes the return type to clarify what the DataFrame contains, which is sufficient.

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

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

The description clearly identifies the resource (AMAC ABS public disclosure information) with a precise source URL and return type, which distinguishes it from other AMAC fund tools in the sibling list. The action is implied via the context of returning a DataFrame, though it does not use an explicit verb like 'retrieve' or 'get'.

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. While the Chinese title clearly states the specific data source, there is no mention of use cases, prerequisites, or comparison with similar AMAC tools like amac_fund_info or amac_fund_sub_info.

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?

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds the data source URL and indicates a pandas.DataFrame return, but does not describe the data structure, update frequency, or any limitations. It provides some context beyond the annotations but not rich behavioral detail.

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

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

The description is compact and front-loaded with the Chinese title, followed by a useful URL and return type. However, the :return: line essentially repeats the title, which is slightly redundant but not excessively wasteful.

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

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

For a no-parameter, no-output-schema tool, the description provides the data source and general topic, which is adequate but not complete. It does not specify the DataFrame columns, row scope, or how the data is organized, which might be needed for an agent to fully understand the output.

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

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

The tool accepts zero parameters, so the schema fully covers parameter semantics. Even though the description offers no parameter details, none are needed. The baseline of 4 for zero-parameter tools 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 specific resource: AMAC's fund company and subsidiary collective asset management product disclosure, with a direct URL. The title and return line both name the exact dataset, distinguishing it from sibling tools like amac_fund_info or amac_member_sub_info.

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

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

The description provides no explicit guidance on when to use this tool versus other AMAC tools. It only gives the dataset name and URL, leaving the agent to infer the use case. No alternatives, exclusions, or context are mentioned.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=true and destructiveHint=false, so safety is covered. The description adds that this is a page-range scrape of an external website returning a pandas.DataFrame, but says nothing about rate limits, scrape slowness, or reliability.

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?

It is a pasted Sphinx docstring: the first line duplicates the annotation title, then the URL, then per-parameter directives. Front-loaded and not bloated, but structurally redundant against the title and marginally wasteful.

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 states the return type (:rtype: pandas.DataFrame), which is useful since there is no output schema. However, it does not describe what columns/content the DataFrame holds or how paging interacts with the defaults, leaving moderate 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?

Schema description coverage is 0%, so the description must carry the parameter burden, and it does document both parameters (start_page = starting page number, end_page = ending page number). It could be stronger by clarifying that the two define a range (the Chinese text is ambiguous, reading as 'data of the specified page').

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 names the source (AMAC), the disclosure section, and the specific resource ('私募基金管理人基金产品' / private fund manager fund products), so an agent knows it lists fund products from the AMAC disclosure portal. It does not, however, differentiate itself from the many other amac_* siblings (e.g. amac_fund_abs, amac_fund_sub_info).

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

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

No when-to-use or when-not-to-use guidance is provided. With 13+ sibling amac_* tools and hundreds of other data tools, the description gives no basis for choosing this one over alternatives.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds the return type (pandas.DataFrame) and a source URL, but no extra behavioral context such as pagination, rate limits, or data update frequency. No contradiction exists.

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

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

The description is somewhat cluttered, mixing a title, a URL, and a docstring-style return type in a single block. It is not front-loaded with a clear 'what' statement and includes redundant elements (e.g., the URL), but it remains relatively short.

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

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

For a zero-parameter read-only tool, the description conveys the data source and return type reasonably well. However, it does not clarify the exact data scope (e.g., all sub-funds, any filtering) or explicitly contrast with sibling fund tools, leaving some ambiguity for an agent deciding if this tool fits a query.

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

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

The tool has zero parameters, and the input schema is empty. According to the rubric, the baseline for 0 parameters is 4, and the description does not need to explain parameter semantics.

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

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

The description clearly identifies the resource as '证券公司私募投资基金' (securities company private equity funds) from the AMAC information disclosure platform. Although it lacks an explicit action verb, the source URL and ':return' imply data retrieval, and the specific fund type distinguishes it from sibling amac_* 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 on when to use this tool versus alternatives. There are many similar amac_* tools (e.g., amac_fund_info, amac_member_sub_info), but the description does not explain how this tool differs or when it is the appropriate choice.

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?

Annotations already indicate a safe, idempotent read operation. The description adds the return type (pandas DataFrame) and source URL, but does not disclose details like pagination, data freshness, or any potential quirks. It is adequate but not rich.

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

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

The description is concise, but it redundantly repeats the Chinese title from the annotations. The URL and return type lines are useful, so the structure is acceptable without being overly explanatory.

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

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

The tool is simple with no parameters or output schema. The description provides source, return type, and a clear label, but lacks details about the actual data columns or content that would help an agent understand what the DataFrame contains.

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

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

The tool has zero parameters, so the description need not explain parameter meanings. The baseline is 4, and there is nothing requiring additional clarification.

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 AMAC futures company collective asset management product disclosure data, with a specific source URL and return type. This distinguishes it from other AMAC tools like amac_member_info or amac_fund_info.

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

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

No guidance is provided on when to use this tool versus alternatives or any context for invocation. It only states the data source and return type, leaving the agent to infer usage from the name.

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

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint and destructiveHint=false, so the safety profile is covered. The description adds the data source URL and the return type (:rtype: pandas.DataFrame), which is genuine extra context, but says nothing about scope (does it return the full list?), pagination, or freshness.

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 resource name is front-loaded, which is good, but the block is a concatenation of a source title, a raw URL, an unusable code table, and Sphinx-style return tags. The URL and code list do not earn their place for an agent that cannot filter by those codes.

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

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

For a zero-parameter, read-only data fetch with annotations covering behavior and no output schema required, the description conveys what dataset is returned and its type. It is largely complete; only the true scope/volume of the returned list is left unspecified.

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 zero parameters the baseline is 4, but the description lists cancellation codes (主动注销:100, 依公告注销:200, 协会注销:300) that cannot be supplied anywhere because the schema takes no arguments. This orphaned mapping adds noise to a parameterless tool rather than clarifying inputs, so it falls below the neutral 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 names a specific, identifiable resource — AMAC's public disclosure of deregistered (已注销) private fund managers — which differentiates it from siblings like amac_manager_info (general manager list) and amac_manager_classify_info. It lacks an explicit verb, but the resource identity is unambiguous enough for an agent to select the 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?

There is no when-to-use statement, no named alternative, and no exclusions or prerequisites. The cancellation-type codes (100/200/300) hint at a filtering concept but give no guidance on when to reach for this tool over the other AMAC manager 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?

Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds the source URL and return type, but does not disclose data fields, pagination, or rate limits. With annotations, the bar is lower, but the description contributes minimal 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 short and front-loaded, containing only the title, source URL, and return type. No redundant text or repetition, so it is concise and well-structured.

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

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

With no output schema, the description should explain the returned data. It states the return type (pandas.DataFrame) and general content (私募基金管理人分类公示), but does not specify columns or detailed categories. This leaves gaps for an agent trying to understand what exactly is retrieved.

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

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

The tool has zero parameters and the schema coverage is 100%. The description adds nothing about parameters because none exist, which is appropriate and matches the baseline for a zero-parameter tool.

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 AMAC private fund manager classification disclosure and provides the source URL, making the resource clear. However, it lacks an explicit verb like 'fetch' or 'download' and doesn't distinguish this tool from sibling AMAC tools (e.g., amac_manager_info).

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

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

No guidance is provided on when to use this tool versus alternatives. There are no exclusions or alternative tool mentions, so the agent receives no context for 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?

Annotations already disclose readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, so the safety profile is covered. The description adds the source URL and return type (pandas.DataFrame), but no additional behavioral details such as pagination, data limits, or freshness.

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

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

The description is very concise and front-loaded with the title, followed by a useful URL and return type. It has no redundant content, but could be slightly improved with a proper explanatory sentence.

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

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

Without an output schema, the description should explain what the returned DataFrame contains (e.g., columns, scope). It only repeats the title and return type, leaving the data structure and the 'comprehensive' nature ambiguous. 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?

The input schema has zero parameters, so the baseline is 4. The description correctly implies a no-input comprehensive query and does not need to elaborate on 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 names the resource and action: '私募基金管理人综合查询' (comprehensive query of private fund managers) from the AMAC website, with a URL and return type. It is specific and distinguishes itself from sibling tools by its focus on manager info, though it lacks an explicit verb and doesn't compare with 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?

There is no guidance on when to use this tool versus other AMAC tools like amac_manager_cancelled_info or amac_member_info. The description only states what it is, not when to prefer it, and no alternatives or exclusions are mentioned.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, covering the safety profile. The description adds that it returns a pandas.DataFrame and identifies the source URL, but does not disclose additional behavioral traits such as pagination or data volume. This is acceptable given annotations, 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 short and to the point, containing only the tool name, source URL, and return type. It is not front-loaded in a structured way but avoids redundancy. Slightly more structure could help, but it earns a high score for brevity.

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

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

With no output schema, the description must convey what the DataFrame contains. It says '会员机构综合查询' (comprehensive member institution query), which gives a general idea but lacks column names or a summary of fields. For a no-parameter tool, this is minimally 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?

This tool has zero parameters, so the schema is empty. The description adds no parameter details because none exist. The baseline of 4 is appropriate as there is nothing to document and the description correctly implies a full dataset fetch.

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 this tool performs a comprehensive query of member institutions from the AMAC (China Securities Investment Fund Industry Association), with a specific resource and verb. It distinguishes itself from sibling tools like amac_member_sub_info by focusing on the top-level 'member institution' 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 implies usage – when one needs AMAC member institution information – and provides the data source URL. However, it does not state when to prefer this over sibling tools or provide any exclusions or alternative recommendations, leaving the agent to infer.

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

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

Annotations already cover read-only, non-destructive, and idempotent behavior. The description adds the return type (pandas.DataFrame) and the source URL, which is useful, but it does not disclose other behaviors like network requirements or data freshness.

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 but somewhat repetitive, repeating the same phrase in the title and the :return: section. It lacks a clear front-loaded summary and the URL placeholder between the title and return value adds clutter.

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

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

While the description provides enough context to understand the general data source, it lacks details about the DataFrame columns or the exact structure of the return. Given no output schema, the description could better explain what data is included.

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

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

The tool has zero parameters, so the schema fully covers the input space. The description correctly avoids discussing parameters, aligning with the baseline for no-parameter tools.

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

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

The description clearly identifies the specific resource: information disclosure for securities companies' private fund subsidiary managers from AMAC. It distinguishes from sibling tools like amac_member_info by the precise entity type, though it lacks an explicit verb like 'query' or 'fetch'.

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

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

The description provides no guidance on when to use this tool versus alternatives. It does not mention exclusions or compare with other AMAC tools, leaving the agent to infer usage from the name and content 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?

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, which covers the safety profile. The description adds the official source URL and indicates it is a public information disclosure, but it does not disclose additional behavioral details such as columns, pagination, or update 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 compact and front-loaded, presenting the source category, URL, and return type in a scannable format. The ':return:' line repeats the content in the first line, but overall it remains 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?

For a zero-parameter public data listing backed by strong annotations, the source URL and return type provide sufficient context. It does not enumerate returned fields, but the low complexity makes that less critical.

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

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

The tool has zero parameters and schema description coverage is 100%, so no parameter descriptions are necessary. The input schema fully defines the empty parameter set.

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 specifies the resource: AMAC public disclosure of bond investment trading related personnel, including the source URL and return type. It also differentiates from the sibling amac_person_fund_org_list by explicitly stating '债券投资交易相关人员公示'. However, it lacks an explicit action verb like 'retrieve' 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 is provided on when to use this tool versus related AMAC personnel, fund, or bond disclosure tools. The description only states the data source and return value, leaving usage decisions to inference from the tool name.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds only the source URL and return type but does not disclose behavioral details like pagination, data freshness, authentication needs, or how the 'symbol' parameter affects the result scope.

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 moderately sized but structured as a docstring with a title, URL, and parameter/return sections. The purpose is not front-loaded as a concise sentence, and the URL and lengthy choice list add bulk without a clear functional summary. It is not overly verbose, but could be improved by adding a one-line operational description at the top.

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

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

With only one parameter and no output schema, the description covers the essentials: parameter choices, type, return type, and source URL. However, it doesn't specify the columns or contents of the returned DataFrame, and it lacks a clear behavioral overview, leaving the agent with an incomplete picture for a simple 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's 'symbol' property has no description (0% schema description coverage), so the description is the sole source of parameter semantics. It provides a comprehensive list of valid choices, the type (str), and the meaning (organization type), which is essential for correct invocation. However, it doesn't explain the default behavior when 'symbol' is omitted beyond what the schema default indicates.

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 a Chinese title '基金从业人员资格注册信息' (fund practitioner qualification registration information) and then provides parameter/return docs, but it lacks an explicit verb phrase stating what operation is performed. The tool name contains 'list', implying retrieval of a list, but the description doesn't clearly say 'list fund practitioners by organization type' nor does it distinguish itself from the similar sibling amac_person_bond_org_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?

There is no guidance on when to use this tool versus alternatives. The description does not mention conditions, prerequisites, exclusions, or name any sibling tools such as amac_person_bond_org_list, making it hard for an agent to decide between similar AMAC 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?

The annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, which cover the safety profile. The description adds the source URL and the pandas.DataFrame return type, which is helpful but does not disclose any additional behavioral traits such as pagination, update frequency, network dependency, or potential errors. This adds some context beyond annotations but is 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 extremely concise, packing the source, exact disclosure type, URL, and return type into one short block. There is no fluff, but the formatting is a single dense paragraph with docstring syntax, which could be slightly more readable if broken into sentences or lines. Still, every piece of information serves a purpose.

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

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

Given that the tool has no parameters, no output schema, and strong annotations, the description is largely complete: it names the exact data set, the source institution, and the return type. Missing details like whether the DataFrame covers all products or only current ones would be nice, but for a simple read-only lookup tool, the provided context 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?

The tool accepts zero parameters, and the input schema is empty with 100% schema description coverage. According to the rubric, a zero-parameter tool gets a baseline of 4. The description correctly notes the return type but does not need to explain parameters, so the baseline 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's scope: it returns AMAC's disclosure of securities companies' collective asset management products (证券公司集合资管产品公示). The name and URL further confirm the resource, and the specific product type distinguishes it from other AMAC tools. However, it lacks an explicit verb (e.g., 'retrieve' or 'list'), instead framing everything as a noun phrase, so it falls just 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?

Usage context is implied by the explicit product type: if an agent needs securities company collective asset management product disclosures from AMAC, this is the tool. But there is no explicit statement about when to use this versus the many other amac_* sibling tools, nor any mention of exclusions, prerequisites, or alternative 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint and destructiveHint=false, so the safety profile is covered. The description adds only the return type (pandas.DataFrame) and nothing about coverage (which countries, what frequency, date range, update cadence, or offline/network requirements) beyond what the openWorld annotation implies.

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 definition is short and the resource name is front-loaded, which is good. However it is formatted as Sphinx docstring directives (:param:, :type:, :return:, :rtype:) that duplicate the schema's type information, and the bare URL is presented without explanation.

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 single-parameter tool with a default and rich annotations, the description is minimally sufficient: the agent knows it passes a country name and gets back a DataFrame. It omits what the agent would want to judge fit — data frequency, historical span, and available country coverage.

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 0%, so the description must carry the burden, and it does: it identifies symbol as a country name (指定的国家名称) and gives a concrete example ('China'). It does not enumerate valid country values, which is the one remaining gap.

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 names the resource (经济政策不确定性指数) and cites the source URL policyuncertainty.com, so an agent can tell it returns an EPU index series. But it uses no verb (fetch/get/list) and gives no scope or differentiation from its nearest siblings (article_ff_crr, article_oman_rv, article_rlab_rv), leaving the purpose implied rather than stated.

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 explicit when-to-use, when-not-to-use, or alternative-tool guidance. The source URL hints at the dataset's domain, but the agent gets no criteria for choosing this tool over the many macro_* siblings that also expose country-level economic indicators.

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?

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds that the return is a single pandas DataFrame, which is useful context about the output format. It does not add deeper behavioral details like pagination or content specifics, but given the strong annotated safety profile, this is acceptable.

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 brief, containing the title, a URL, and return type information. However, the raw URL is a large block that disrupts readability and does not directly support tool invocation. The structure is minimally acceptable but could be improved by placing the URL on a separate line or integrating it as a reference.

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

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

With no output schema and no parameter schema, the description is the sole source of information about what the tool returns. It states 'FF多因子模型单一表格' and 'pandas.DataFrame' but does not explain what factors are included, the table's columns, or any other relevant details. The URL hints at the data source but does not sufficiently describe the actual output structure. This is incomplete for an agent to know what to expect.

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 is empty with zero parameters, so there are no parameter semantics to elaborate. Baseline for zero params is 4, and the description does not need to compensate for any missing parameter documentation. The description's mention of a single table and DataFrame provides some output context.

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 the FF multi-factor model and states that it returns a single table, which implies a retrieval operation. The inclusion of the Ken French data library URL adds context. However, it does not explicitly differentiate from sibling tools or state a verb like 'get', so it is clear but not fully distinguishing.

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

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

The description provides no explicit guidance on when to use this tool over alternatives. It neither mentions exclusions nor suggests similar tools like article_epu_index. Usage is only implied by the name and the content, which is insufficient for an agent to decide between many article_* 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?

Annotations already mark it as read-only, idempotent, and non-destructive, so the bar is lower. The description adds meaningful context: the data comprises daily non-parametric realized volatility measures, these are not forecasts, and the data is provided 'as is' for informational purposes. This goes beyond the annotations and helps set expectations about the data's nature.

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 bloated with boilerplate text copied from the Oxford-Man website, including background on research, disclaimers, and 'visually explored' commentary that is not relevant to tool invocation. The useful parameter lists and symbol table are embedded in this verbose prose, and the table is truncated, making it poorly structured for quick consumption.

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 two-parameter data-fetch tool, the description provides the symbol list, index list, and an availability table, which is helpful for understanding coverage. However, it lacks details on the returned DataFrame's structure (columns, date range) and is cut off mid-table, leaving the content incomplete. It does not clarify the difference from the sibling short version.

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 0%, so the description must compensate. It lists all valid values for both symbol and index, and provides a table mapping symbols to asset names and availability date ranges. This gives agents the ability to select valid parameters, though it does not explain what each index metric (e.g., medrv, rk_twoscale) represents.

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 this tool provides data from the Oxford-Man Realized Library, with parameters for symbol and index, and returns a pandas.DataFrame. The verb is implicit ('数据' meaning 'data'), but it is evident that it retrieves realized volatility data for a given symbol and index. It does not distinguish itself from the sibling tool article_oman_rv_short.

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 is provided on when to use this tool versus alternatives such as article_oman_rv_short or article_rlab_rv. The table of availability dates gives coverage information but does not explain selection criteria or scenarios for choosing this tool.

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

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

Annotations already declare read-only/idempotent behavior, so the bar is lower. The description adds context that the data is daily non-parametric measures, which helps interpret the output. However, it doesn't state behavior like invalid symbol handling or whether the 'short' version truncates data, leaving some 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 includes a long boilerplate paragraph about the Oxford-Man library, its research, and disclaimers that is not needed for tool invocation. Key info (param, return) appears early, but the essay-like content inflates the description 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 tool has one optional parameter and no output schema. The description explains what realized measures are conceptually but doesn't specify the DataFrame structure, date range, or what differentiates the 'short' version from the full version. This is a meaningful gap.

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 has zero description coverage, so the description compensates by listing valid symbol values (FTSE, GDAXI, RUT, SPX, STOXX50E, SSEC, N225) with index names. This is directly useful for parameter selection. The default is not mentioned, but the param explanation is solid for the single parameter.

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 (Oxford-Man Realized Library) and states the return type, but lacks an explicit verb like 'retrieve' or 'get'. It also doesn't clarify what 'short' means relative to the sibling tool article_oman_rv, so the tool's specific function is ambiguous.

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 choose this tool over article_oman_rv or article_rlab_rv. The long text describes the library's background but never mentions use cases, alternatives, or exclusions.

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

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

While annotations already declare readOnly, idempotent, and non-destructive behavior, the description introduces ambiguity by specifying ':return: pandas.DataFrame' but showing a sample output that is clearly a pandas Series (with 'Name: RV, Length: 5810, dtype: float64'). It also mentions multiple RV estimates (5-min and 15-min) without clarifying what the actual output structure is, creating confusion about the return format.

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

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

The description is extremely verbose, containing a large block of website copy including references, methodology details, and data cleaning information that is irrelevant for an agent invoking the tool. It is not front-loaded with a crisp summary, and the repeated sections make it difficult to parse quickly. Only the param/return snippet and the objective sentence are useful.

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

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

With only one optional parameter and no output schema, the description should clearly define the return value and parameter constraints. It fails to do so—the return type is ambiguous (DataFrame vs Series), the symbol format is undefined, and the data coverage limitations (e.g., US equities after 2007) are not explicitly stated. The sample output helps but is inconsistent with the declared return type.

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 one parameter 'symbol' with no description (0% coverage). The description only says ':param symbol: str 股票代码' (stock code), which adds minimal meaning. It does not explain the expected format (e.g., US ticker vs. numeric code), valid symbols, or how the default '39693' is used, leaving the agent to guess what value to supply.

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 'up-to-date daily annualized realized volatilities for individual stocks, ETFs, and future contracts' from the Risk Lab website, which is a specific resource. The title and sample output reinforce this, but it lacks an explicit comparison to sibling RV tools (e.g., article_oman_rv), so it does not fully distinguish from 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 explicit guidance is given on when to use this tool versus other RV tools or when not to use it. The description implies it is for realized volatility data, but does not state any context, exclusions, or alternatives, leaving the agent to infer usage from the data source and methodology.

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?

Annotations already declare readOnlyHint, idempotentHint, openWorldHint and destructiveHint=false, so the safety profile is covered. The description adds that results are ordered from first to last page and returned as a pandas.DataFrame, but says nothing about rate limits, auth, or failure 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 purpose is front-loaded in the first line, which is good, but the body is epydoc boilerplate (:type/:rtype lines) that repeats parameter information and pads the definition. It is serviceable rather than tight.

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

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

With no output schema, the description usefully states the return is a page-ordered collection of the disclosure tables. For a read-only scraping tool with three optional params, this is adequate but thin on how 'page' and 'begin' interact and on default 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 0%, so the description must carry the load and it does so partly: it documents 'page' (fetch first N pages) and supplies the allowed 'item' values {"机关","本级","分局本级"} that the schema lacks. But 'begin' is left as the vague '开始页面' with no explanation of how it differs from 'page', and defaults are unexplained.

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 names a specific verb (获取/get) and a concrete resource (首页-政务信息-行政处罚-银保监分局本级行政处罚信息公开表), so an agent knows exactly what dataset is returned. It is clear but offers no differentiation from siblings, mainly because no genuinely competing tool exists 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?

It implicitly tells the agent that pagination must be resolved first via ak.bank_fjcf_total_page(), which is a useful usage hint. However it never states when to use this tool versus alternatives or any prerequisites, so guidance is implied rather than explicit.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=true, and destructiveHint=false, so the safety profile is covered. The description adds that the return is a list of selectable options (可选项列表), a modest behavioral note, but nothing about what the options represent or how they are scoped. With annotations doing the heavy lifting, a 3 is appropriate.

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?

It is short, but the content is docstring fragments (a bare URL, ':return:', ':rtype:') rather than a front-loaded sentence about what the tool does. Concise but not well-structured for an agent.

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

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

For a zero-parameter enumerator with no output schema, the description does say the result is a list of options, which is minimally sufficient. But the ambiguous '非指定期限部分' scope and lack of any return-shape detail leave it only adequate.

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

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

The tool takes zero parameters, so there is no parameter semantics to document; per the rubric this is a baseline 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 identifies the data source (中国债券信息网-中债指数) and states it returns a list of available options (可选项列表), which aligns with the name 'available_index'. However, the phrasing '非指定期限部分' is obscure and the purpose is conveyed as a raw source pointer with a URL rather than a clean verb+resource statement, leaving the exact scope ambiguous.

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 indication of when to use this tool versus alternatives is given. Siblings like bond_composite_index_cbond, bond_new_composite_index_cbond, and bond_treasury_index_cbond exist but are not referenced, so the agent has no routing guidance beyond the tool name.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety and idempotency. The description adds no behavioral details beyond a source URL, such as rate limits, data update frequency, or dependencies. The URL provides provenance but not operational behavior.

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

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

The description is compact, with a title, URL, and docstring, and is front-loaded with the title. The URL line is not well integrated into the description, and the docstring is presented in a standard but somewhat mechanical format. It is concise but could be more readable.

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 one parameter and no output schema. The description covers the purpose, the parameter's meaning, and the return type (pandas.DataFrame) via the docstring. It misses market-specific details (e.g., Shanghai vs. Shenzhen) and does not mention any filtering options or data scope, but it is adequate for a basic historical data fetcher.

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 the meaning '质押式回购代码' (pledged repo code) to the symbol parameter, which is useful since the schema only provides type and default. However, it does not elaborate on valid code formats, examples beyond the default, or how the code maps to a specific market.

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 historical pledged repo data from East Money, using the term '历史数据' (historical data) and a source URL. It specifies the resource type (质押式回购) and distinguishes from other bond tools by focusing on historical data. However, it lacks an explicit verb phrase like 'fetches' or 'returns'.

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 bond_sh_buy_back_em or bond_sz_buy_back_em. There is no mention of exclusions, prerequisites, or specific use cases beyond the generic historical data 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safe read-only nature is covered. The description adds the source URL and return type but does not disclose additional behavioral traits such as data freshness, pagination, or any limitations beyond what the annotations provide.

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 succinct, with one line of source context and a short docstring for parameters and return. It is not overly verbose, though the placement of the URL in the first line slightly affects readability.

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

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

For a simple tool with one parameter and no output schema, the description provides the essential source and return type. However, it lacks details on the exact contents of the returned DataFrame, expected date format, and any edge cases, making it minimally acceptable 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 0%, so the description must compensate. It provides the parameter 'date' with type str and a brief Chinese description '指定日期' (specified date), which adds some meaning. However, it does not specify the expected date format (e.g., YYYYMMDD), though the default value '20210111' hints at 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 providing bond cash market overview data from SSE, with the return type '债券成交概览' (bond transaction overview). It distinguishes itself from sibling tools by specifying the SSE source and the specific overview data, though it lacks an explicit verb like 'get' or 'fetch'.

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 only gives a source URL and parameter documentation, without mentioning exclusions, prerequisites, or scenarios where other bond tools would be more appropriate.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds the return type (pandas.DataFrame) and source URL, but it does not mention behavioral traits like pagination, rate limits, or error handling. The added context is modest but not contradictory.

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, containing a title, URL, and docstring. It is front-loaded with the purpose. However, the title duplicates the annotation title, and the URL may be extraneous for agent use, slightly reducing efficiency.

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 one-parameter tool with no output schema, the description provides essential elements: data source, parameter meaning, and return type. It does not describe the contents of the adjustment records or any caveats, but it is mostly adequate for basic selection.

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 0%, so the description must compensate. It says ':param symbol: 可转债代码', which conveys that symbol is the convertible bond code. This is minimal and does not explain format, examples, or how to find valid codes beyond the schema default.

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 resource (可转债转股价调整记录) and indicates it returns a pandas.DataFrame, but it lacks an explicit action verb like 'get' or 'fetch'. It reads as a label rather than a clear function. It is distinguishable from siblings like bond_cb_redeem_jsl by focusing on adjustment logs.

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

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

No guidance is provided on when to use this tool versus alternatives. It includes a data source URL but no exclusions or comparisons with sibling tools like bond_cb_jsl. The only implicit context is that it is for adjustment records.

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?

Annotations already declare the tool read-only, idempotent, open-world, and non-destructive, so the description only adds the source URL and return type. It does not describe the DataFrame's columns or time range, but the annotations cover the key safety and side-effect aspects.

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

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

The description is very concise, with the resource name and URL front-loaded, and the return type lines are minimal. However, it is fragmented into disconnected pieces (title, URL, return annotation) rather than a cohesive sentence, slightly reducing readability.

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

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

For a tool with no parameters and strong annotations, the description provides the essential information: the exact index name, source, and return type. It does not specify the structure of the returned DataFrame (e.g., daily values, columns), so an agent may not know exactly what data to expect, but it is sufficient for a basic 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 schema has zero parameters, so there is nothing to explain beyond the absence of inputs. The baseline score of 4 is appropriate because the description adds no misleading parameter information and it clarifies the return type.

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 exact resource: the Jisilu convertible bond equal weight index, and the ':return:' line explicitly states that this tool returns that index. It is differentiated from sibling bond tools by naming the specific index and source, though it lacks an explicit verb like 'get' or 'fetch'.

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 compared to alternatives such as bond_cb_jsl or bond_index_general_cbond. There are no exclusions, alternative suggestions, or contextual cues to help an agent decide when this is the right choice.

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 annotations already declare the tool as read-only, idempotent, and non-destructive, covering the safety profile. The description adds a critical behavioral detail: it requires a browser cookie for invocation, which is not captured by the annotations. This is significant context for the agent, though it does not explain why the cookie is needed or what happens if it is absent.

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 includes the URL, parameter, and return type, but it is formatted as a docstring with tags rather than a clear natural-language explanation. It is not bloated, but it lacks a clear imperative statement of purpose.

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

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

Given the single optional parameter and clear annotations, the description covers the basic invocation details. However, it does not describe the contents of the returned DataFrame, any authentication nuances, or how the tool behaves with or without the cookie, which could leave the agent uncertain about the tool's output.

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

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

With 0% schema description coverage, the description provides the only semantic meaning for the cookie parameter, stating it is a browser cookie to input. This is helpful, but it omits details such as whether the cookie is optional, its format, or how to obtain it, leaving some ambiguity.

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 simply repeats the tool's name ('集思录可转债') and provides a URL, without any action verb or explicit statement of what the tool does. It does not differentiate from sibling tools like bond_cb_index_jsl or bond_cb_adj_logs_jsl, leaving the agent to infer its 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, nor any exclusions or prerequisites beyond the cookie parameter. It only states the resource source, making it difficult for an agent to decide among the many bond-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?

Annotations already declare read-only and non-destructive behavior, so the safety profile is covered. The description adds the return type (pandas DataFrame) but discloses no other behavioral traits such as data freshness, limitations, error handling, or assumptions about the market identifier prefix.

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 includes essential components: a clear title, sample URL, parameter definition, and return type. The URL might be slightly redundant but adds a concrete example, and the overall length is appropriate.

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

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

For a tool with one optional parameter and no output schema, the description covers the return type and parameter format adequately. However, it lacks details about the contents of the returned DataFrame, valid market prefixes, or any usage guidance, leaving some gaps for a fully informed decision.

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 0% description coverage for the symbol parameter, so the description compensates well by explaining it as a convertible bond code with market identifier and providing a concrete default and example URL. This gives the agent the context needed to format the parameter correctly.

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 (convertible bonds from Sina Finance) and the action (retrieve detail/profile information), supported by an example URL. It does not explicitly mention an alternative or contrast with sibling tools, but the name and content together make the purpose unambiguous.

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

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

No guidance is provided on when to use this tool versus other bond-related tools. The description only states what it does and the parameter, without any use-case context or comparison to alternatives.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows it's a safe read operation. The description adds a return type (pandas.DataFrame) and a source URL, which provides a little extra context. However, it does not disclose behavioral traits such as whether the data is live-snapshot, how the DataFrame is structured, or any potential rate limits or authentication needs. Thus it provides partial but not rich additional behavior 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 very short, but it redundantly repeats the title in the first line and the return line. The URL is a useful reference, and the return-type annotation is informative. The structure is compact, but the redundant repetition reduces conciseness quality; every sentence does not fully earn 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?

For a highly specialized bond tool with no output schema, the description leaves important gaps: it does not explain what '强赎' (strong redemption) means, what columns/fields the DataFrame contains, or how this tool differs from similarly named bond tools. The URL hints at the data source but does not make the tool self-contained for an agent. Given the tool's simplicity (no params) and good annotations, more context is still expected to make it complete.

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

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

The tool has zero parameters, and the input schema is empty (100% schema coverage with no properties). The baseline for zero-parameter tools is 4, and the description offers no parameter information because none exists. It does not need to compensate for parameter semantics, so the score reflects 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 is essentially a repeat of the title ('集思录可转债-强赎'), providing no additional specification of what the tool does beyond the name. The URL and return type annotation add minimal context, but the core purpose (retrieving strong-redemption convertible bond data) is only implied by the title. This is a tautology of the title, hence a low score.

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

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

There is no guidance on when to use this tool versus alternatives. It does not mention use cases, preferred conditions, or exclude scenarios. Sibling tools like bond_cb_jsl (general convertible bonds) and bond_cb_index_jsl (index) suggest similar resources, but the description offers no differentiation or selection advice.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds that it returns a pandas DataFrame and gives an example URL, but it does not disclose error behavior, data scope details, or potential limitations. No contradiction with annotations.

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 with source URL, parameter docstring, and return type. The first line repeats the title but does not waste many words. It is appropriately sized for a simple tool.

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

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

For a one-parameter read-only tool, the description gives the source, parameter format, and return type. However, it does not specify what fields the '债券概况' DataFrame contains, and given the many closely related convertible bond tools, more detail on the exact output content would improve completeness.

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

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

The input schema provides no description for the 'symbol' parameter (0% coverage). The description compensates by explaining that symbol is a convertible bond code with a market identifier (带市场标识的转债代码) and offers a default example 'sh155255', which helps the agent construct a valid argument.

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

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

The description clearly states the tool fetches a convertible bond overview (债券概况) from Sina Finance, with an example URL. The resource and data type are clear, but it does not explicitly distinguish itself from sibling tools like bond_cb_profile_sina or bond_zh_cov, so it misses full differentiation.

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

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

There is no guidance on when to use this tool versus other convertible bond tools, no prerequisites, and no alternatives mentioned. The description only provides parameter semantics, leaving the AI agent without context for choosing this tool.

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

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint and destructiveHint=false, so the safety profile is covered. The description adds genuine behavioural context beyond that: the source site URL, the period enum values, and the hard constraint that start_date and end_date must not span more than one month.

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 purpose sentence is front-loaded and the raw source URL is useful, but the Sphinx-style block is disordered – types are attached to the wrong parameters – which hurts scannability. Content is reasonably sized with no obvious 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 4-parameter, no-output-schema tool with annotations present, the description covers the purpose, all four parameters and the key date-range constraint. It still omits the valid symbol values, the shape/columns of the returned DataFrame, and any pagination or rate-limit behaviour, so it 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 description coverage is 0%, so the description must carry the load; it does name all four parameters (symbol as the indicator, period as the term interval with choices {'0.1','0.5','1'}, start/end date with the 1-month limit). However, the :param/:type pairs are mismatched (the period enum is listed under symbol and vice versa), and the allowed value domain for symbol is never given beyond the 国债 default, leaving gaps.

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 a specific resource – closing yield curve historical data (收盘收益率曲线历史数据) – which clarifies the otherwise cryptic name 'bond_china_close_return'. It does not differentiate from near-neighbours such as bond_china_close_return_map or bond_china_yield, 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?

There is no when-to-use guidance, no prerequisites, and no mention of the closely related sibling tools (bond_china_close_return_map, bond_china_yield). The agent must infer applicability entirely from the name and the resource 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 only restates the return type and provides a URL, adding no behavioral context beyond the annotations (readOnlyHint, openWorldHint, idempotentHint). It does not disclose data scope, pagination, scraping behavior, or other traits, so the agent cannot anticipate side effects or limitations.

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

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

The description is short but contains redundancy: '收盘收益率曲线历史数据' appears in both the first line and the :return: field. The URL is useful, but the repeated phrases could be consolidated without loss of information, making it slightly inefficient rather than taut but 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 is inadequate for selecting this tool among many bond-related siblings. It does not explain the 'map' in the tool name, the bond type implied by the URL (bondType=CYCC000), or the data's scope. Despite having no parameters and annotations, the missing differentiation and source details leave the tool's purpose 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?

With 0 parameters and schema coverage of 100%, the baseline is 4. The description adds no parameter information, but none is needed since the tool accepts no arguments. The semantics are fully determined by 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 is a tautology: it repeats the title '收盘收益率曲线历史数据' twice and provides a URL, but does not state a specific action or resource beyond the title. It also fails to distinguish from the related sibling tool bond_china_close_return, making the purpose ambiguous, especially regarding what 'map' means.

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 bond_china_close_return or bond_china_yield. The description lacks any context about use cases, prerequisites, or exclusions.

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

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false. The description adds the one-year date-window limit and return type (pandas.DataFrame), but omits auth/rate-limit or column-level 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 title, note, and Sphinx-style fields are front-loaded and mostly relevant, but the two raw URLs and repeated return phrasing add noise without helping invocation.

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

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

There is no output schema, so the description should be more complete about return values, yet it only gives rtype pandas.DataFrame. It does provide the key date-range constraint, which is enough for basic invocation but leaves gaps.

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

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

Schema description coverage is 0%, so the description must carry parameter meaning. It documents both start_date and end_date and the one-year constraint, but the wording about each date returning a one-year window is confusing and no date format (YYYYMMDD vs YYYY-MM-DD) is specified.

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?

Names the source (中国债券信息网) and resource (国债及其他债券收益率曲线), so the agent knows it fetches bond yield-curve data. It does not explicitly differentiate from sibling bond tools such as bond_china_close_return_map, but the resource is specific enough.

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?

Includes a concrete constraint (end_date - start_date should be less than one year) and parameter behavior, implying a date-range query. It gives no explicit when-to-use guidance or alternatives versus sibling bond/macro 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, covering the safety profile. The description adds that it returns a pandas DataFrame of the composite index, but it does not disclose additional behavioral traits such as data coverage, update frequency, or output structure.

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, URL, and param/return documentation. Although the parameter lists make it lengthy, each entry is necessary. It is front-loaded with the index name and source.

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

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

Without an output schema, the description should explain the return structure in more detail. It only says '新综合指数' (new composite index) and rtype pandas.DataFrame, leaving the DataFrame columns and their meaning vague. Parameter coverage is thorough, but the return value semantics are under-specified.

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 two string parameters with defaults but no descriptions or enums. The description fully compensates by listing all valid choices for both indicator (17 options) and period (14 options), making it clear what values are accepted and what each represents.

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 exact target as the ChinaBond Composite Index (中债-综合指数), including the source URL and hierarchy. This distinguishes it from sibling bond index tools like bond_index_general_cbond and bond_new_composite_index_cbond.

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 retrieving the ChinaBond Composite Index through its title and parameter documentation, but it does not explicitly state when to use this tool versus alternatives, nor does it provide any exclusions or context about preferability.

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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is known. The description adds a source URL and states the return type as pandas.DataFrame, which is useful but minimal. It does not disclose data freshness, pagination, or any site-specific quirks. Without annotations, this would be insufficient, but annotations carry the core safety burden.

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, consisting of a breadcrumb, a URL, and a short docstring. It front-loads the purpose and includes a source link. However, the structure is slightly awkward, mixing a human-readable heading with code-style docstring, and the copy-paste error in the end_date description detracts from clarity. It is not verbose, but not well-polished.

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

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

There is no output schema, so the description should describe the returned data structure, but it only says '企业债发行' as a generic return label. It also omits date format details, allowed ranges, timezone considerations, or any limitations of the data source. Given the tool has just two parameters and a simple return, the description leaves the agent with insufficient information for correct invocation and interpretation.

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 0%, so the description must compensate. It attempts to do so with ':param start_date: 开始统计时间' and ':param end_date: 开始统计时间', but both are described as the 'start time', which is clearly an error for end_date. This misleads the agent about the semantics of end_date. Even the start_date description is vague ('统计时间' could mean many things), and no date format constraints beyond the defaults are given.

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

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

The description clearly identifies the resource as '巨潮资讯-数据中心-专题统计-债券报表-债券发行-企业债发行' (Cninfo Data Center - Bond Reports - Bond Issuance - Corporate Bond Issuance). Although it lacks an explicit verb like 'fetch' or 'retrieve', the combination of the breadcrumb and return type makes it evident this returns corporate bond issuance data. It distinguishes itself from sibling bond issuance tools (e.g., bond_cov_issue_cninfo, bond_local_government_issue_cninfo) via the specific '企业债发行' subcategory.

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

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

The description provides no guidance on when to use this tool versus alternatives. It does not mention use cases, prerequisites, or exclusions. The only contextual hint is the breadcrumb, which implies it is for corporate bond issuance, but there is no explicit 'when to use this, use X for other bond types' 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds the source URL and return type but does not disclose additional behavioral traits such as rate limits or data freshness. No contradictions with annotations.

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 includes the essential source, URL, and return type. It follows a lightweight docstring format with clear lines, though it could be more polished as 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 no-parameter tool with strong annotations, the description is adequate. However, it does not specify what columns or data the '比价表' includes, which would help differentiate it from similar bond comparison tools in the sibling list.

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

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

The tool has zero parameters, so there is no parameter semantics to explain. Schema description coverage is trivially 100%, and the description appropriately omits parameter details.

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

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

The description names a specific resource (东方财富网可转债比价表) and indicates the return type (pandas.DataFrame). It is distinct from sibling tools by the '比价表' term, but lacks an explicit verb like 'get' or 'fetch', making the action implicit.

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 other convertible bond tools. There are no alternatives, exclusions, or context cues to help an agent decide 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds that the tool returns a pandas DataFrame, which is useful but does not disclose other behavioral details such as pagination, rate limits, or the exact structure of the returned 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 structured as a docstring, containing only the essential elements: a title, URL, parameters, and return type. It avoids unnecessary verbosity, though the duplicated typo and URL could be considered minor clutter.

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

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

With no output schema, the description should explain the return value more concretely. It only says ':return: 可转债发行' (returns convertible bond issuance), which is vague. The parameters and annotations for read-only behavior are adequate, but the lack of detail about the resulting DataFrame columns or scope leaves some gaps.

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

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

The description documents the parameters with ':param start_date: 开始统计时间' and ':type start_date: str', which adds meaning beyond the schema. However, for end_date it erroneously repeats '开始统计时间' instead of '结束统计时间', providing misleading semantics for that parameter. This typo undermines the usefulness of the parameter 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 identifies the tool's purpose through the title line '巨潮资讯-数据中心-专题统计-债券报表-债券发行-可转债发行', which specifies the resource (convertible bond issuance) and source (CNInfo). It distinguishes itself from sibling bond issuance tools by naming '可转债发行' specifically. However, it lacks an explicit action 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?

The description provides no guidance on when to use this tool versus alternatives. It simply states the data source and topic, with no mention of scenarios, exclusions, or sibling tools. The intended use must be inferred entirely from the title and tool name.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so safety is covered. The description adds only that it returns a pandas.DataFrame, which is useful but minimal. No additional behavioral context like pagination, data scope, or authentication needs is 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 extremely terse, bordering on under-specification. It consists of a title-like path, a URL, and a return type label, with no complete sentence. While there is no fluff, the lack of structure or explanatory prose hurts 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?

With no output schema and no parameters, the description carries the full responsibility of explaining what data is returned. It only says '可转债转股' (convertible bond stock conversion) and 'pandas.DataFrame', without describing columns, data granularity, or any other contextual details. This is insufficient for an agent to understand the tool's output.

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

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

There are zero parameters, so the description bears no burden in explaining parameter behavior. The baseline of 4 applies because with no parameters, no semantic gaps exist.

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 is essentially a navigation path ('巨潮资讯-数据中心-专题统计-债券报表-债券发行-可转债转股') with no explicit verb or action. It implies the tool returns convertible bond stock conversion data, but it reads as a label rather than a clear statement of purpose. It does not distinguish itself from sibling tools like bond_cov_issue_cninfo.

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

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

The description provides no guidance on when to use this tool versus alternatives. There is no mention of preferred use cases, context, or exclusions, leaving the agent without direction to select among many similar bond issuance 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?

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering the safety profile. The description adds the source URL and return type, but does not disclose any further behavioral traits such as data granularity, date range limitations, or potential missing data. It adds some value beyond annotations but is 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 brief and follows a structured docstring format with URL, param, and return sections. Every line serves a purpose, and there is no fluff. It could be improved by leading with an explicit action verb, but it is efficiently 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?

The tool is simple with one parameter and no output schema, but the description is still thin. It does not describe what columns or content the returned DataFrame contains, nor the date format or any specifics about the bond turnover overview. The source URL is helpful, but an AI agent would need more detail to correctly interpret the output.

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

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

Schema description coverage is 0%, so the description must fully compensate. It only provides a minimal '指定日期' (specified date) and type str, with a default example '20210104'. It does not explicitly explain the date format (YYYYMMDD) or whether the parameter is optional beyond the default. The compensation is inadequate.

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 clearly: SSE bond market turnover overview, with a source URL. Although it lacks an explicit verb like 'retrieve' or 'fetch', the docstring format and return type imply data retrieval. It partially distinguishes from siblings via the tool name 'deal_summary' vs 'cash_summary', but the description itself 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?

No guidance is provided on when to use this tool versus alternatives. There is no mention of exclusions, prerequisites, or context in which the tool is appropriate. The only hint is the date parameter, but there is no explanation of typical usage scenarios.

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?

Annotations already provide readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. However, the description adds little beyond a URL and return type. It introduces phantom 'sector and indicator' fields that don't exist in the schema, and doesn't disclose pagination behavior, data scope, or any limitations beyond the page parameter.

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 includes a useful URL and parameter doc, but the structure is not front-loaded with a clear purpose sentence. The return statement about 'sector and indicator' is misleading and wastes a sentence, reducing overall clarity despite the brevity.

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

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

With no output schema, the description should explain what the returned DataFrame contains, but it doesn't describe columns, rows, pagination size, or any filtering beyond the page number. The incorrect 'sector and indicator' reference makes the tool's behavior incomplete and potentially misleading for the agent.

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

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

The schema has one 'page' parameter with 0% description coverage. The description does clarify that page is a numeric page number, but this contradicts the schema's string type. It also references 'sector' and 'indicator' which are not parameters, adding confusion. The description only partially compensates for the lack of 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 identifies the NAFMII registration system and provides a URL, but lacks a clear verb like 'query' or 'list'. It mentions returning 'data for specified sector and indicator' which is misleading because the only parameter is 'page'. This provides a general sense of resource, but doesn't clearly distinguish from other bond data tools.

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

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

No guidance on when to use this tool versus alternatives. The description is essentially a docstring with no context about use cases, prerequisites, or exclusions. It doesn't mention any sibling tools or when this NAFMII source 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?

Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds a source URL and return type, but does not disclose additional behaviors such as whether data is historical or real-time, or any rate limits. This is acceptable given the annotations, but not rich.

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

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

The description is a concise docstring-style entry with the title, source URL, parameter documentation, and return type. Every part provides necessary information with no fluff, and the structure is immediately scannable.

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

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

For a simple single-parameter data retrieval tool, the description provides a source URL, allowed symbols, and return type. However, it does not clarify whether the returned DataFrame contains historical or current yields, nor the columns included, leaving some ambiguity for the agent.

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

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

The input schema has no description for the 'symbol' property, but the description compensates by fully enumerating all 13 allowed choices (e.g., 美国10年期国债). This adds significant meaning beyond the schema, even though it doesn't explain the semantic differences between maturities.

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

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

The description explicitly states the tool provides US Treasury yield market data from Sina Finance, which identifies the resource and function. However, it does not distinguish itself from sibling tools like bond_gb_zh_sina or bond_zh_us_rate, so it lacks full differentiation.

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

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

The description implies usage by naming the data source and listing acceptable symbol values, but it does not provide explicit guidance on when to use this tool versus alternatives, nor does it state any exclusions or prerequisites.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is established. The description adds the source URL and the list of valid symbols, which is useful, but it does not disclose output structure, data granularity (historical vs. current), or any additional behavioral traits.

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

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

The description is a compact docstring with a title, URL, and param/return sections. It is appropriately sized and front-loaded, containing no waste, though the Chinese wording is somewhat terse.

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

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

With no output schema, the description should explain the return structure. It only states a DataFrame of yield market data without specifying columns or whether it's historical or current. The URL points to the data source page, which helps, but the description is not 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?

The schema has no enum or description for the symbol parameter, and the description compensates by enumerating all valid choices (中国1年期国债 through 中国30年期国债). This provides the agent the necessary vocabulary to invoke the tool correctly, though it doesn't explain the meaning of the values (e.g., maturity periods).

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 Sina Finance China treasury bond yield market data, with a specific resource and source. However, it doesn't explicitly differentiate from sibling bond yield tools like bond_china_yield, relying on the name for distinction.

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

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

The description provides no guidance on when to use this tool versus alternatives such as bond_china_yield or bond_zh_us_rate. Usage is implied from the parameter list and return type, but no explicit context or exclusions 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?

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds no behavioral context beyond the annotations—no mention of web fetching, rate limits, authentication, or potential failure modes. It does not contradict annotations, but also provides no additional 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 docstring with :param: and :return: sections, which is reasonably structured but not front-loaded with a high-level summary. It includes useful parameter choices and the URL but is somewhat verbose for a tool description.

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

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

With no output schema, the description provides the return type and the general meaning. It covers parameter choices and references the index list tool, but lacks context on data shape, date handling, error behavior, and differentiation from sibling bond index tools, making it only minimally complete.

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

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

The schema has no descriptions or enums (coverage 0%), but the description lists all allowed values for indicator and period, and references available_bond_index() for index_category. This adds essential meaning beyond the schema, though it does not explain the semantics of each choice.

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 it returns '指定指数的指定指标的指定期限分段数据' (segment data for a specified index, indicator, and period) from the China Bond Index family (中债指数族系). This is a clear purpose, but it does not differentiate from sibling tools like bond_composite_index_cbond or bond_new_composite_index_cbond, so it lacks sibling distinction.

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

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

There is no guidance on when to use this tool versus other bond index tools. The only hint is a reference to available_bond_index() for index_category, but no explicit comparisons or exclusions are provided.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the description's burden is reduced. Yet it adds only the source URL and Chinese parameter labels, not behavioral context like filtering semantics, pagination, or optionality of parameters, beyond the generic return type.

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 docstring that repeats the title string, includes a URL, and lists parameters without a concise summary sentence. It is not front-loaded with a clear purpose statement, making it less efficient than a well-structured natural language description.

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

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

For an 8-parameter tool with no output schema, the description fails to explain parameter optionality, result format, or typical usage. It only provides the return type as 'pandas.DataFrame' and a vague '信息查询结果', leaving an agent without enough context to 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?

The description provides Chinese glosses for each parameter (e.g., '债券名称' for bond_name, '债券代码' for bond_code), adding semantic meaning beyond the bare schema names. However, it lacks format constraints, examples, or guidance on parameter combinations, so it only partially compensates for the 0% schema description coverage.

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

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

The title '中国外汇交易中心暨全国银行间同业拆借中心-数据-债券信息-信息查询' clearly indicates a bond information query tool from ChinaMoney, providing a specific resource and implied verb. However, it does not differentiate from sibling tools like bond_info_cm_query and bond_info_detail_cm, and lacks a direct sentence stating its 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?

The description contains only a URL and parameter definitions, with no guidance on when to use this tool versus alternatives, no prerequisites, and no exclusions. An agent cannot determine the intended context 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?

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is clear. The description adds the return type (pandas.DataFrame) and the source URL, but does not describe output structure or any edge cases. No contradictions with annotations.

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 mixes a long Chinese title, a URL, and docstring-style parameter/return fields. It is somewhat redundant (the phrase 查询相关指标的参数 appears both in the header and the return description). The structure is acceptable but not tightly 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?

With only one parameter and no output schema, the description covers the basic function and parameter choices. However, it does not explain what the returned DataFrame contains or how the output might be used in conjunction with other bond tools. The behavior is simple enough that 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 description coverage is 0%, so the description must compensate. It explicitly lists the valid choices for symbol: {主承销商, 债券类型, 息票类型, 发行年份, 评级等级}, which is critical since the schema has no enums. It does not explain the meaning of each indicator or output format beyond the names, but the choices are self-explanatory.

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 queries parameters for indicators from China Foreign Exchange Trade System (中国外汇交易中心暨全国银行间同业拆借中心). The listed symbol choices (主承销商, 债券类型, etc.) clarify what kinds of parameters are returned. It is fairly specific but could be clearer that it is a helper for retrieving valid index parameter values, and the Chinese-language title may be less informative for non-Chinese users.

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

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

No explicit guidance on when to use this tool versus alternatives. The description does not mention that it can be used to obtain valid symbol values for sibling tools like bond_info_cm or bond_info_detail_cm. The URL provides a source but 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?

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds a source URL and return type, but no behavioral context such as data freshness, network requirements, or limitations. It does not contradict annotations.

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 starts with a long hierarchical label, then a URL, then a docstring. Important information is buried and not front-loaded. It could be rewritten as a clear sentence like 'Get bond details by bond symbol, returning a pandas DataFrame.'

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 one parameter and no output schema, but the description does not explain what fields are in the returned DataFrame or how to interpret the bond details. It also does not mention the relationship between symbol and the URL's bondDefinedCode, leaving gaps for an agent.

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

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

The description explicitly documents ':param symbol: 债券简称', clarifying that symbol means 'bond short name' beyond the raw schema. This is valuable since schema description coverage is 0%, and the default example gives a concrete format.

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 is a menu path ('数据-债券信息-信息查询-债券详情') rather than a clear verb+resource statement. It implies retrieving bond details but does not explicitly say 'get details for a given bond symbol' or distinguish from sibling tools like bond_info_cm or bond_info_cm_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?

There is no guidance on when to use this tool versus alternatives. No mention of prerequisites, scenarios, or exclusions. The description is purely a source path and docstring with no 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering safety traits. The description adds the data source URL and return type (DataFrame), but does not disclose additional behavioral details such as rate limits, pagination, or date range handling.

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 relatively short, but the first line repeats the tool name as a title, and the parameter documentation is inconsistent with a typo. The structure is standard docstring format but could be more polished and error-free.

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

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

The tool is a simple data retrieval with two parameters and no output schema, so the description should provide more detail on the returned data structure. It only states '地方债发行' as the return without specifying columns, and the parameter descriptions are incomplete due to the typo.

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 includes doc comments for both parameters, but they are identical and the end_date parameter is incorrectly described as '开始统计时间' (start time) instead of end time. The schema provides defaults implying YYYYMMDD format but no explicit description, so the description both adds and undermines clarity.

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 CNInfo's thematic statistics for local government bond issuance (地方债发行) with a return type of pandas.DataFrame. It distinguishes from sibling tools like bond_corporate_issue_cninfo and bond_treasure_issue_cninfo by focusing specifically on local government bonds.

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 that this is for querying CNInfo data on local government bond issuance with a date range, implying its use for historical bond issuance data. However, it does not explicitly mention alternatives or when not to use this tool, so it lacks 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds the data source URL and the index hierarchy, which gives context about the data origin. However, it does not disclose behaviors like rate limits, pagination, date range coverage, or network dependency, leaving some gaps beyond what annotations provide.

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 line breaks separating the source, parameters, and return information. It is not overly verbose, though the first line repeats the hierarchical name which also appears in the annotations' title. The parameter lists are essential and formatted compactly. Overall, it is efficient and readable.

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 relatively simple with two optional parameters and no output schema. The description states the return type (pandas DataFrame) and the return value (新综合指数), but does not detail the DataFrame's columns, the time range of data, or how parameters affect the output. Annotations cover safety, but for a complete understanding, more behavioral and output context would be helpful. It is minimally adequate.

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

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

The schema provides only parameter names with defaults and no descriptions (0% schema coverage). The description compensates exceptionally by listing all valid choices for both indicator and period, which are critical for correct invocation. It does not explain the meaning of each choice, but the labels are self-explanatory in context. This adds significant value beyond the bare 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 specific resource: 中国债券信息网-中债指数-中债指数族系-总指数-综合类指数-中债-新综合指数, along with a URL. The return type and value are also specified, making the tool's purpose evident. However, it lacks an explicit action verb like 'fetch' or 'query', but the context implies data retrieval, so it's clear but not perfectly articulated.

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 the many similar bond index tools in the sibling list (e.g., bond_composite_index_cbond, bond_treasury_index_cbond). It does not mention alternatives, exclusions, or typical use cases. The only context is the specific index name, which differentiates it somewhat, but explicit usage guidance is absent.

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?

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, covering safety. The description adds that it returns a pandas.DataFrame, but does not disclose other behavioral aspects like real-time status or whether it returns the full universe of instruments. Minimal extra context beyond structured 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 extremely compact: a title line, a source URL, and return type documentation. It is front-loaded and contains no filler words, making it easy to parse at a glance.

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 zero parameters and no output schema, the description is largely sufficient: it names the data source, market segment, and return type. However, it omits any details about columns or data frequency, which would be useful for an agent to fully understand the returned data without additional inference.

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

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

There are zero parameters, so schema coverage is trivially 100%. The baseline for 0 params is 4, and the description does not need to explain any parameters—it remains appropriately silent on that front.

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 Shanghai Exchange pledged repo data from East Money, with a specific URL and return type. The name and description distinguish it from the sibling bond_sz_buy_back_em (Shenzhen) and historical buyback variants.

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 context: it is for Shanghai pledged repo from a specific East Money page. It does not explicitly name alternatives or exclusions, but the 'sh' in the name and the explicit '上证' make the Shanghai scope and primary use case obvious.

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?

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds that the tool returns a pandas.DataFrame, which is useful context, but does not disclose further behavioral details such as data time ranges or potential latency. No contradiction with annotations.

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, containing a descriptive title, a source URL, and return type information. No filler or redundancy, though it could be slightly more structured to separate the data source from the tool's 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 simple no-parameter tool with good annotations, the description provides the return type and source but lacks details about the data's time period (e.g., current vs. historical) or column structure since there is no output schema. This 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?

The tool has zero parameters, so the description need not explain any. It does state the return type and data source, which is sufficient for a parameterless tool. Baseline for 0 params is 4.

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

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

The description clearly identifies the resource as '现券市场成交行情' (spot bond market transaction quotes) from the China Foreign Exchange Trade System, with a source URL. It distinguishes from siblings like bond_spot_quote by focusing on executed transactions rather than quotes, though it lacks an explicit verb like 'fetch' or 'get'.

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

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

No guidance is provided on when to use this tool versus alternatives. There is no mention of use cases, exclusions, or comparison to sibling bond tools, leaving the agent to infer applicability from the resource name alone.

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

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

The description includes the source URL and return type, which offers some context about data origin, but it does not disclose any behavioral traits like data freshness, pagination, or potential network dependencies. The annotations already declare read-only and non-destructive behavior, so the description adds little beyond that.

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

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

The description is very short and contains only a source URL and return type, which is efficient but lacks structural prose. It is not bloated, though it is under-specified in other dimensions.

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

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

No output schema exists, so the description should clarify what the returned DataFrame contains. It merely repeats '现券市场做市报价' (spot market-making quotes), which adds little beyond the tool name, leaving the row/column structure and data scope 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?

The tool takes no parameters, and the input schema is empty with 100% coverage. The description does not need to explain parameter semantics, so the baseline of 4 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 identifies the tool as providing spot market-making quotes from China Money, but it does so in a fragmentary way with no explicit verb. It restates the tool name with minimal elaboration, making it hard to distinguish from sibling bond spot tools without deeper inspection.

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 similar bond quote tools. There are no exclusions, alternatives, or context cues beyond the source URL.

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?

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds little beyond the source URL and return type, without detailing what data is included, whether it is historical or real-time, or any other behavioral traits.

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

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

The description is brief but repetitive, with '深证质押式回购' appearing in both the title and the return line. It is structured as a docstring but lacks a clear, front-loaded sentence stating the tool's purpose.

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

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

There is no output schema, so the description should explain the return value in more detail. It only states that the return type is a pandas DataFrame and vaguely mentions '深证质押式回购', leaving the column structure and data scope 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?

The tool has zero parameters, so the description does not need to explain parameter meaning. The baseline of 4 applies because there is no parameter documentation burden.

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 '深证质押式回购' (Shenzhen pledged bond repo) and provides a data source URL, but lacks an explicit verb such as 'get' or 'fetch'. It distinguishes from the Shanghai sibling via the name, but the purpose is inferred rather than clearly stated.

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

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

There is no guidance on when to use this tool versus alternatives like bond_sh_buy_back_em. The description only states the data source and return type, with no contextual hints for 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?

Annotations already declare this as a safe read-only, idempotent operation, so the bar for additional behavioral disclosure is lower. The description adds the data source URL and return type but does not disclose behavioral traits like date range handling, pagination, rate limits, or whether the returned data is a snapshot. No contradiction with annotations is present.

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

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

The description is compact and well-structured, with a clear title-like path, a URL, and standard parameter documentation blocks. It is not overly verbose, and each element serves a purpose, though the URL could be considered extraneous for an AI agent.

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

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

This simple two-parameter tool has no output schema and relies on the description to convey essential usage context. While the description identifies the data source and general purpose, it omits critical details such as the date format expectation and any note about the date range's inclusivity or exclusivity, leaving an agent with incomplete information to call 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?

Schema coverage is 0%, so the description must compensate for parameter meaning. It provides Chinese labels for start_date ('开始统计时间') and end_date ('结束统计数据'), which adds some semantic value beyond the raw schema. However, it does not specify the required date format (e.g., YYYYMMDD), despite defaults suggesting a numeric format, leaving room for user error.

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 specifies the resource as '国债发行' (treasury bond issuance) from Cninfo's data center, which clearly identifies the data domain. The name and title further confirm the tool's purpose, though the description lacks an explicit verb like 'retrieve' or 'list'. It differentiates from siblings by the 'treasury' qualifier, but does not explicitly contrast with other issue 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?

Usage is implied by the description's path and the tool name, indicating this is for treasury bond issuance data. However, there is no explicit guidance on when to use this tool versus sibling tools like bond_corporate_issue_cninfo or bond_local_government_issue_cninfo, nor any mention of alternatives or exclusions.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds the data source URL and output type (pandas.DataFrame) but no additional behavioral details such as pagination, rate limits, or data coverage. No contradiction with annotations exists.

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

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

The description is relatively compact but front-loads a verbose source path and URL that are not essential for invoking the tool. The parameter and return documentation are useful, yet the overall structure reads as a docstring rather than a purpose-driven description.

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

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

The tool has two parameters and no output schema, so the description should clarify output columns, date coverage, and uniqueness among siblings. It only states the return is a 国债指数 (treasury bond index) DataFrame, which is insufficient for an agent to understand the data without additional inspection.

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 zero description coverage for parameters, so the description must compensate. It lists allowed values for indicator (full price, net price, wealth) and period (various maturity ranges), which is helpful, but it does not explain the meaning of each indicator or the structure of the returned data. This is partial compensation but leaves semantic gaps.

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 consists of a Chinese source path and return type with no explicit verb, so it only implies the tool retrieves the ChinaBond Treasury Bond Index. It names the resource precisely but does not explicitly state the operation or differentiate it from sibling bond index tools like bond_composite_index_cbond or bond_index_general_cbond.

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. Sibling tools include multiple bond index tools, but the description provides no selection criteria, exclusions, or context to help an agent choose this specific treasury index tool.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds only the source URL and return type, with no details about data contents, columns, or expectations. This goes slightly beyond annotations but lacks substantive behavioral context.

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

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

The description is short and has no padding, but it is mostly a hierarchical title followed by a URL and return type. It is not front-loaded with an action or purpose, and the repetitive title could be considered waste. It earns a middle score for brevity without efficiency.

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

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

With no output schema and no parameters, the description is the only source of context. It fails to explain what specific convertible bond data is returned, such as columns, time range, or whether it is a snapshot. Given the abundance of sibling bond tools, this is insufficient for an agent to confidently select this tool.

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

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

The input schema has zero parameters, so the baseline for parameter semantics is 4. The description provides no parameter-specific details, which is appropriate since none exist. No further compensation is needed.

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 is essentially a title: 'Eastmoney Data Center - New Stock Data - Convertible Bond Data'. It states the resource and source but lacks a specific verb like 'get' or 'list'. The return type indicates it returns convertible bond data, but this doesn't clearly distinguish it from numerous sibling convertible bond tools such as bond_zh_cov_info or bond_cov_comparison.

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 alternative bond tools. No mention of use cases, exclusions, or alternatives. The description simply names the data source and return type, leaving the agent to guess when this is appropriate.

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