gk-pay-skills-mcp

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

There are no annotations, so the description carries the burden. It usefully discloses the per-use cost (¥0.05) and states that the output is a structured review, but it does not describe other behavioral traits such as data source, formatting details, or failure behavior. This is acceptable for a simple generator but not rich disclosure.

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

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

The description is three short sentences with no filler: purpose, input/output, cost, and parameter example. The most important scoping information is front-loaded.

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

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

For a single-parameter tool with an output schema present, the description covers what the agent needs to invoke it correctly: the exact input meaning, an example, the return mode, and the cost. The output schema handles return-value details, and no extra behavioral context is required.

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 type string for q with 0% description coverage, so the description must compensate. It does: '输入盘面描述' defines the parameter as a market description, and the q example ('沪指涨0.8% 半导体领涨 量能放大') demonstrates the expected form. No syntax edge cases are given, but the core semantics are clear.

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 verb and resource: it '生成' a structured A-share morning review from a market-description input. The domain and output format distinguish it from the sibling content-generation tools, so an agent can tell what it does without opening schema.

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

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

The description implies the intended usage: provide a market description (盘面描述) such as the q example and receive a structured review. However, it never explicitly says when to prefer this tool over alternatives or lists exclusions, so the guidance is only implied.

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

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

With no annotations provided, the description carries the behavioral disclosure burden. It usefully discloses that the tool returns a concise interpretation and costs ¥0.05 per call, but it does not mention side effects, permissions, rate limits, or what happens on invalid input. This is acceptable for a simple paid lookup tool but not comprehensive.

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

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

Every sentence earns its place: it states the function, defines the input, describes the output, and discloses the cost. The purpose is front-loaded and there is no filler.

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

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

For a simple one-parameter tool with an output schema present, the description covers the essential call information: input semantics, output type, and cost. It lacks explicit alternative routing and detailed input syntax, but the low complexity and existing output schema keep these omissions minor.

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 gives only a bare string parameter `q` with no description, and schema coverage is 0%. The description compensates by explaining that `q` should contain Ten Gods/Heavenly Stems-Earthly Branches keywords and provides a concrete example ('正官'). It does not specify formatting for multiple keywords, but for a single-parameter tool this is strong compensation.

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 verb and resource: '八字快速解读' (quick Bazi interpretation), and clarifies the exact input type ('十神/干支关键词') and output type ('简明解读'). This clearly distinguishes it from the sibling ziwei_life_read, which targets a different fortune-telling system.

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

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

The description implies usage by specifying the input format and the 'quick' nature of the tool, but it never explicitly states when to prefer this over alternatives such as ziwei_life_read or diagnose, nor does it provide any exclusion criteria. The 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?

No annotations are provided, so the description carries the burden. It discloses that it uses empirical formulas and a base of 380V three-phase, and mentions a cost of ¥0.05 per call. However, it doesn't disclose any side effects (none expected), rate limits, or output details, which is acceptable for a calculation tool.

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

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

The description is concise, with each line defining a parameter compactly. The cost is noted upfront, and defaults are provided. Slight trade-off in structure: the description is a bullet list but not separated by newlines in the text, yet it remains 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?

Given the tool has an output schema (unseen), the description doesn't need to explain return values. It covers the essential parameters and their purposes, and the pricing is a useful contextual detail. It's complete for a calculation tool with a clear scope.

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 define parameters. It explains type (cable, breaker, motor), power (kW required), voltage (default 380), and length (used for cable, default 50). This adds meaning beyond the schema, which only defines types without choices.

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

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

The title and description indicate a calculation tool for electrical selection (cable, breaker, motor) based on empirical formulas. The purpose is clear, but it doesn't explicitly distinguish from siblings, though siblings are in different domains (e.g., bazi_quick, diagnose).

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

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

The description states the default voltage and length, and indicates that length is used for cable type. However, it doesn't explicitly state when to use this tool vs. alternatives, but given the sibling domain differences, usage context is 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?

The description discloses the pricing (¥0.05 per call) and the output behavior (ranked troubleshooting plans), which is useful. However, with no annotations provided, the description carries the full burden of behavioral disclosure. It doesn't mention whether the tool is read-only, whether it calls external APIs, or what the exact output format looks like. The output schema exists but the description doesn't elaborate on it.

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 core purpose, followed by parameter guidance and pricing. The examples are useful but the parameter explanations could be slightly more structured. Overall, every sentence earns its place.

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

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

Given the tool's simplicity (2 required string params, output schema present), the description covers the essential usage. It includes pricing, parameter semantics, and output behavior. It could be more complete by describing the output schema structure or any limitations, but for a simple diagnostic tool it is largely sufficient.

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

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

Schema description coverage is 0%, so the description must compensate. It does: it explains brand as device brand with examples (g120 / s7-1200 / fx5u / hmi / modbus) and symptom as fault symptom with examples (F07452 / 通讯不通 / 启动不转 / 报警灯闪). This adds significant meaning beyond the bare schema, though it doesn't specify exact allowed values or formats.

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: diagnosing industrial control faults by taking a device brand and fault symptom, and returning troubleshooting plans sorted by match. It also includes concrete examples for both parameters, which makes the purpose unmistakable.

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

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

The description provides clear usage context: input brand and symptom, get ranked troubleshooting plans. It also gives example values for both parameters, which helps the agent know what to pass. It doesn't explicitly state when not to use it or name alternatives, but the sibling list shows distinct tools, so the usage context is reasonably clear.

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

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

With no annotations, the description carries the full transparency burden. It adds useful behavioral details beyond the schema: per-use cost (¥0.05) and expected input structure. However, it does not disclose output format details, potential limitations, or any safety/reversibility context, which would be valuable for a generation tool.

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

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

The description is tight and front-loaded: purpose, input, output, and price all appear in two short sentences, followed by a compact example. No filler or redundancy.

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

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

For a single-parameter generation tool with an output schema, the description covers the essentials: what to input, what to expect, and the cost. It could add more detail about how to structure multiple selling points or language expectations, but the provided example mitigates this 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 description coverage is 0%, so the description must compensate. It does so by explaining that q should contain 'product and selling points' and provides a concrete example ('thermos cup, long-lasting insulation 12h, food-grade 316'). This adds real meaning beyond the bare string type, though it leaves the exact separator format ambiguous.

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: input product and selling points, receive conversion-oriented copy, with the domain 'e-commerce selling point copy'. This differentiates it from content tools like toutiao_killer_title or xhs_viral_note, though it doesn't explicitly name those alternatives. A specific verb and resource are present.

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 showing an example input format ('保温杯 长效保温12h 食品级316'), but it gives no explicit guidance on when to choose this tool over siblings like xianyu_listing_pro or toutiao_killer_title. There are no exclusion criteria or alternative routing instructions.

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

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

With no annotations, the description carries the full burden. It discloses the cost per call (¥0.05) and that it returns a structured outline. It doesn't mention side effects, data handling, or rate limits, but for a simple generative tool the disclosed cost and output type add meaningful behavioral context beyond the schema.

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

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

The description is two sentences with zero filler: the core function and example come first, followed by the cost. Every word earns its place, and the structure is immediately scannable 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?

Given there's an output schema (not shown but flagged) and a single well-described parameter, the description covers the essentials: what it does, example input, and cost. It doesn't mention edge cases or failure modes, but for a deterministic outline generator with a simple interface, this is likely sufficient.

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

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

Schema coverage is 0%, so the description must compensate. It explicitly states the parameter 'q' is a theme/genre and provides a concrete example ('都市重生'), adding meaning that the bare string type in the schema lacks. This gives an agent the necessary clue to supply a valid input.

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

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

The description clearly states the tool returns a chapter-by-chapter outline from a theme input, with an explicit example ('都市重生'). This verb-resource pairing distinguishes it from unrelated siblings like calc or diagnose, leaving no ambiguity about 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 implies usage: if you need a novel outline from a genre/theme, use this tool. It doesn't explicitly contrast with alternatives or state exclusions, but given the sibling tools are all distinctly different domains, the implied context is sufficient for an agent to select it.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explicitly mentions the cost per use (¥0.05), the operation's purpose, and that it returns a mapping, which implies a non-mutating behavior. It could mention potential error cases or limits, but the core behavior is transparent.

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 front-loaded: the main purpose appears first, followed by input format, output type, cost, and a useful example. Every sentence serves a distinct purpose with no filler.

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

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

The tool is simple (one parameter) and has an output schema, so the description does not need to explain return values. It covers the input format, the purpose, and the cost, which is the most critical operational detail. Nothing an agent needs to invoke it correctly is missing.

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 only a string parameter with 0% description coverage. The description fully compensates by explaining that the input must be a comma-separated variable list and gives a concrete example ('Motor1_Run,Valve3_Open,...'). This adds essential 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?

The description states the tool generates Chinese annotations for PLC variable tables, with a clear verb ('生成') and resource ('PLC 变量表'). It includes a concrete example of input format and distinguishes itself from the unrelated sibling tools.

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

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

The description clearly indicates when to use the tool (when Chinese comments for PLC variables are needed) and provides the exact input format with an example. It does not explicitly state when not to use it, but the sibling tools are clearly unrelated, so the context is sufficiently clear.

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

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

With no annotations, the description carries the full burden. It discloses the operation (listing), the exact scope (all 13 paid skills), and an operational detail (0.05 yuan per call, Alipay A2M metered billing). It does not explicitly say the call is read-only, but '列出' makes that reasonably clear.

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

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

A single sentence that front-loads the action, then gives specifics, then the purpose. Every clause earns its place, and there is no filler or redundant phrasing.

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 is a simple parameterless catalog; the output schema covers return structure, and the description supplies scope, pricing, billing model, and purpose. Nothing needed for correct invocation is missing.

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 schema coverage is trivially 100% and there is nothing for the description to add. This matches the stated baseline for 0-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 names the exact resource (all 13 paid skills) and the verb (列出/list), and states its purpose (discovering available capabilities). It clearly stands apart from the sibling skill tools, which are individual capabilities rather than a catalog.

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 phrase '用于发现可用能力' gives clear context for when to call this tool: before selecting among the available skills. It does not explicitly list exclusions or alternatives, but for a catalog tool that purpose statement is sufficient guidance.

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

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

With no annotations provided, the description carries the full burden. It does disclose the core behavior (returns bilingual/alias mappings), the input type, and the per-call cost (¥0.05), which is useful. However, it does not mention behavior for unknown terms, output structure, or whether the operation is purely read-only beyond the implication of '返回'.

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 one short sentence containing purpose, input example, output type, and cost. Every clause earns its place, and the core purpose is front-loaded. There is no redundant filler.

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

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

For a simple one-parameter lookup tool with an output schema, the description covers the essential aspects: what domain it covers, how to invoke it, what it returns, and its cost. The main gaps are usage routing and error behavior, but these are not critical for a low-complexity tool.

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

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

Schema coverage is 0%, so the description must compensate for the undocumented 'q' parameter. It does add meaning by identifying q as a '术语' and providing a concrete example ('定时器'), but it stops short of specifying accepted formats, language support, or matching behavior. The compensation is partial but adequate for such a simple single-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 states a specific resource ('工控术语') and operation ('对照映射'), and clearly explains the input-output pattern: input a term like 定时器 and receive Chinese/English and alias equivalents. It is clear and actionable, but it does not explicitly differentiate this tool from related siblings such as plc_annotate, so it stops short of a 5.

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

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

The description implies a lookup use case ('输入术语...返回对照'), but provides no explicit guidance about when to choose this tool over alternatives, no exclusions, and no mention of related tools like plc_annotate. An agent must infer appropriate usage from the domain 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?

没有 annotations，因此描述承担行为披露责任。它额外披露了每次调用费用 ¥0.05、输入为话题、输出为标题组，这些是有价值的调用行为信息。不过未说明返回标题数量、错误情况或限制，但结合简单生成类工具和 output schema 的存在，已算充分。

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

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

一句话内依次给出核心定位、输入示例、返回结果和价格，信息密度高且无冗余，适合 agent 快速解析。

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?

对仅有一个必填参数、无复杂嵌套、且有 output schema 的工具，描述已覆盖调用所需的核心信息：输入语义、返回内容、费用。没有明显影响正确调用的缺口。

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 对参数 q 的描述覆盖率为 0%，但描述中用“输入话题（如 房贷利率下调）”补充了 q 的语义，并给出具体示例。单参数场景下这足以让 agent 理解如何填写。

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

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

描述使用明确动词+资源：“输入话题……返回高点击标题组”，并给出具体示例（房贷利率下调），清楚说明这是一个头条标题生成工具。与 viral_hook、xhs_viral_note 等兄弟工具在平台和用途上可区分。

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

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

描述隐含了使用场景：当用户需要一个话题的头条爆款标题时使用，但没有显式说明何时不该用、或与其他标题类兄弟工具的取舍。缺少 when/when-not 的明确指导。

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

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

No annotations are provided, so the description carries the full burden. It discloses the core behavior: input theme and platform, return a counter-intuitive opening hook, and it adds a pricing detail (¥0.05 per call). However, it does not mention whether outputs vary between calls, any content constraints, 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?

Compact and front-loaded: function, output, cost, then parameter examples. Every sentence adds value and there is no repetition of the title or generic 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 two-parameter generator with an output schema, the description covers the essential invocation information: inputs, output type, and cost. It is slightly incomplete only in not differentiating usage from sibling tools or describing parameter constraints, but it is sufficient for a straightforward generation call.

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

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

Schema description coverage is 0%, so the description must compensate. It does so by giving concrete example values for both theme (PLC调试/副业/护肤) and platform (抖音/小红书/头条), adding meaningful context beyond bare string properties. It stops short of specifying exact formats or accepted values, so not a perfect 5.

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 (生成) and resource (爆款钩子), and clearly defines the output as an opening counter-intuitive hook built from theme and platform. The phrase '开篇反常识钩子' distinguishes it from sibling copy tools like toutiao_killer_title and xhs_viral_note, which focus on full titles or notes.

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 such as ecom_selling_copy, toutiao_killer_title, or xhs_viral_note. The examples for theme and platform imply general usage, but an agent must infer the appropriate selection without any direct instruction.

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

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

With no annotations provided, the description carries the behavioral burden. It usefully discloses the per-call cost (每次 ¥0.05) and the input/output shape. It does not describe how content is generated, whether it reflects real Xiaohongshu data, or any limitations, but for a simple stateless generation tool this is acceptable but not rich.

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

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

The description is very short and front-loaded with the core function, followed by an example and pricing. The only minor issue is the repetition of 种草笔记 in both the opening noun phrase and the return clause.

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 an output schema present, the description provides the essential invocation information: what q means, what the tool returns, and the cost. No critical missing detail blocks an agent from calling the tool correctly.

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

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

The schema only defines q as an unannotated string, so the description compensates by explaining that q should be a product/topic and providing the example 平价护肤. This adds meaning beyond the raw schema, though it leaves format constraints unstated.

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: enter a product/topic and receive a Xiaohongshu seeding note. It includes a concrete example (平价护肤), which helps an agent understand what kind of query is expected. It does not explicitly differentiate itself from sibling content-generation tools, so it loses the fifth point.

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 intended use is implied: use this when you need a Xiaohongshu-style recommendation note for a product or topic. However, there is no explicit guidance about when to choose this tool instead of related siblings like viral_hook or toutiao_killer_title.

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

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

With no annotations, the description carries the behavioral disclosure burden. It adds useful context by stating the cost per call (¥0.05) and indicating the call is a generation operation that returns title and description, implying no side effects. It does not detail auth or error behavior, but for a simple generation tool this is reasonably transparent.

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

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

The description is two concise sentences with no filler. It front-loads the purpose, then states input, output, and cost. Every sentence contributes directly to tool selection and 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?

Given one parameter and the presence of an output schema, the description provides the essential input semantics, output form, and cost information. It could be more explicit about output language or input restrictions, but the platform context and example make the intended usage sufficiently clear.

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% and the schema only provides a string parameter q with no description. The description compensates by explaining that q is a product input and gives a concrete example ('九成新Switch'), which conveys the expected format and content. It could add length or formatting constraints, but the guidance is sufficient for a 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 clearly states a specific function: generating high-conversion Xianyu product copy from a product input and returning a title plus description. It differentiates itself from sibling tools by naming the Xianyu platform and the exact output format.

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 a user needs Xianyu product listing copy, aided by the example input. However, it does not explicitly state when not to use it or mention alternatives such as ecom_selling_copy or xhs_viral_note, so routing guidance is only implied.

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

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

With no annotations, the description carries the behavioral burden. It does disclose the per-call cost (每次 ¥0.05) and the general nature of the output (命理解读), which is useful. It does not mention side effects, authentication, rate limits, or response format, but for a simple reading tool the cost disclosure adds meaningful transparency.

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

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

The description is two short sentences with no filler. It front-loads the core purpose, then gives usage and pricing in minimal space. Every sentence contributes essential information.

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

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

For a one-parameter tool with an output schema, the description covers the main requirements: what the tool does, what input is expected, and what it costs. The main gap is the lack of differentiation from the closely related bazi_quick sibling, which could affect tool 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?

The schema provides no description for the required q parameter, so the description must compensate. It does so by defining q as a keyword and giving concrete examples (紫微/命宫). This makes the parameter actionable despite 0% schema coverage.

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

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

The description clearly states the resource (紫微斗数命盘) and the action (解读), and notes that the user supplies a keyword to receive a fortune-interpretation result. It is not a tautology and would be recognizable as a distinct Ziwei tool, but it does not explicitly distinguish itself from sibling bazi_quick.

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: input a keyword like 紫微 or 命宫 and get an interpretation. However, it gives no explicit guidance on when to choose this tool over bazi_quick or other siblings, and no exclusion conditions or prerequisites are provided.

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