sanhao-global-experts

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

No annotations are supplied, so the description carries the full disclosure burden, and it discloses almost nothing about behavior. The only concrete hint is that the result contains a prompt, usage guide, and edge-case handling, but it never states what the tool actually executes, what it costs, or what the caller receives.

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?

Three short sentences that are concise in length but not in value: they are promotional copy ("produce professional results in minutes") rather than front-loaded functional information. The smallest amount of space is spent on the least useful content.

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

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

With no annotations and no output schema, the description is the only source of behavioral and return-value information, and it leaves the actual output undefined beyond "prompt pack." For a tool intended to be invoked by an agent, that is not enough to call it confidently.

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

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

The single optional "query" parameter is fully documented in the schema (100% coverage), so the baseline is 3. The description adds no meaning about how the query scopes or influences the generated prompt pack.

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?

"Turn expertise into output" and "api gen tool prompt pack" restate the tool name in marketing language without naming a concrete operation, resource, or output format. An agent cannot tell from this text whether the tool generates API code, returns a reusable prompt, or produces a usage document.

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 routing against the obvious siblings dt-api-check-tool and dt-api-tpl-tool. The sentence about containing a "usage guide" describes the payload, not when an agent should select 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?

No annotations are provided, so the description must disclose behavior. It hints at a deliverable ('copy-paste prompt pack with examples') but does not say whether the tool generates, retrieves, or transforms content, whether it has side effects, or what the response contains.

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?

Only two sentences, but they are marketing copy rather than front-loaded functional detail. Phrases like 'production-ready', 'run today', and 'for marketing and operations leaders' do not help an agent select or invoke the 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?

With no annotations and no output schema, the description should clarify return format and invocation context. It instead leaves the tool's actual output and role among the dt-api siblings 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?

The sole optional query parameter is fully documented in the schema as 'Optional user query to scope the analysis.' The description adds no parameter meaning beyond that, so baseline 3 is appropriate for 100% schema coverage.

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

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

The description restates the tool name with 'api tpl tool system' and adds generic marketing benefits, but never states a specific action such as generating, retrieving, or filling an API template. It also fails to distinguish this tpl tool from siblings like dt-api-check-tool or dt-api-gen-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 guidance on when to use this tool, when to prefer dt-api-check-tool or dt-api-gen-tool, or what input is needed. 'For marketing and operations leaders' is an audience label, not an invocation condition.

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 behavioral burden, yet it discloses nothing about read/write behavior, side effects, permissions, output format, or rate limits. The text is promotional and gives no operational 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 but front-loaded with marketing ('Save hours every week') instead of purpose. Neither sentence helps an agent understand what to invoke or when.

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

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

For a low-complexity tool with one optional parameter and no output schema, the description still fails to explain what an 'arch check' produces or how it differs from related tools. The marketing framing leaves the core capability undefined.

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 fully documents the single optional 'query' parameter, so the baseline is 3. The description adds no additional meaning about how the query scopes the analysis or what form it should take.

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 repeats the name ('arch check tool') and adds marketing claims about saving time and restaurant/brand growth, but it does not state a specific operation or what the tool actually checks. It does not distinguish the tool from siblings such as dt-arch-gen-tool or dt-arch-tpl-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 guidance, no exclusions, and no explicit routing to alternatives. 'Works in any LLM chatbot' is a product claim rather than invocation 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 at all, the description carries the full disclosure burden and largely fails it: nothing is said about permissions, side effects, whether anything is persisted, or the shape of the response. The single useful signal is that output arrives as a 'full prompt kit with step-by-step instructions', which hints at return content, but the '30-day support promise' is commercial filler rather than 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?

Three sentences, of which at least two ('AI-powered ... pack', '30-day support promise') are promotional and carry no selection value. The lead position is spent on a misleading category label rather than on what the tool returns.

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 mechanically simple (zero required params, no output schema, no annotations), but the description still leaves the agent without knowing what a 'workflow' or 'prompt kit' output looks like or how to choose it over the check/tpl siblings. For a generator in a dense family of similarly named tools, that gap is material.

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

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

Schema description coverage is 100% for the single optional 'query' parameter, so the schema already documents it and the baseline of 3 applies. The description adds nothing about how the query scopes the generation, and notably the schema's 'scope the analysis' wording does not align with the generate framing in the description.

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 opening line calls it a 'restaurant & brand intelligence pack', which has no evident relation to the name dt-arch-gen-tool or to the dt-arch-check-tool / dt-arch-tpl-tool siblings. The phrase 'generate a ready-to-use arch gen tool workflow' gestures at a verb and a resource but is circular ('arch gen tool' generating an 'arch gen tool workflow'), so an agent cannot determine what artifact is produced. No differentiation from the check and tpl variants is offered.

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 statement of when to use this tool, when not to, or which sibling (dt-arch-check-tool, dt-arch-tpl-tool) to pick instead. The only usage-adjacent remark, 'no prompt engineering needed', is a marketing claim rather than a selection criterion.

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 behavioral-disclosure burden, but it does not describe side effects, permissions, return shape, or whether the tool retrieves, generates, or formats anything. It only vaguely says the output includes a prompt, usage guide, and edge-case handling, which is insufficient for safe selection.

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 it is front-loaded with generic slogans rather than the tool's actual function. Its length is appropriate, yet the sentences are mostly marketing and do not earn their place as operational guidance.

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

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

The tool has a simple one-parameter schema and no output schema, so the description needs to explain what the tool actually does and returns. It instead stays at a promotional level and never clarifies the operation, leaving the definition incomplete for an agent deciding whether to invoke it.

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

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

Schema description coverage is 100%, and the only parameter is already documented as an optional query to scope the analysis. The description adds no additional meaning about the query parameter, so the baseline of 3 applies.

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

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

The description relies on marketing phrasing such as "Turn expertise into output" and "produce professional results in minutes" rather than naming a concrete operation and resource. "Prompt pack" hints that it may return a template, but it does not distinguish this tool from siblings like dt-arch-gen-tool or dt-arch-check-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 guidance, no prerequisites, and no alternatives named among the many sibling tools. The target audience mention ("restaurant and brand teams") does not tell an agent when to select this tool over dt-arch-check-tool or dt-arch-gen-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?

With no annotations, the description carries the full behavioral disclosure burden, but it only vaguely hints that the output is a 'copy-paste prompt pack with examples'. It does not clarify whether the tool reads code, requires code context, performs an actual check, or what side effects or return behavior to expect.

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 loaded with vague marketing filler such as 'production-ready', 'run today', and 'structured, repeatable workflow'. The useful information is not front-loaded because the core action of the tool is never clearly stated.

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 annotations or output schema, the description still fails to explain the central operation: what is being checked, what input is needed, and what the agent should expect back. It is not complete enough to guide correct invocation.

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

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

The single optional 'query' parameter is fully documented in the schema, so the baseline is 3. The description adds no additional meaning about how the query should be scoped or what it affects.

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 largely restates the tool name as a 'codereview check tool system' and then adds marketing language about a 'prompt pack'. It does not explain what the check evaluates, what input it expects, or how it differs from sibling tools like dt-codereview-gen-tool and dt-codereview-tpl-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 explicit guidance on when to use this tool instead of its siblings, nor when not to use it. The target audience phrase 'for marketing and operations leaders' does not function as a usable selection condition for an agent choosing among many check/gen/tpl tools.

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

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

No annotations are provided, so the description must carry the full behavioral burden. It discloses that the tool supplies a prompt/playbook and requires no setup, but it does not explain what is generated, how to invoke it meaningfully, or what the agent should expect back, and the restaurant/brand-growth framing conflicts with the code-review name.

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 its sentences are marketing fluff rather than useful specification. "Save hours every week" and the unrelated restaurant/brand-growth claim do not earn their place in a tool definition.

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

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

There is no output schema and no annotations, so the description needs to explain the tool's behavior and return value. Instead it is misleading and omits what the tool generates, leaving an agent unable to determine whether or how to call it for code review work.

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 single optional "query" parameter is fully documented in the schema, so the schema already handles parameter semantics. The description adds no meaningful interpretation of the parameter, but baseline 3 is appropriate given 100% 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 description never states what a code review generation tool actually does. It names the tool as a codereview gen toolkit but then describes a "prompt and operating playbook for restaurant/brand growth," which is misleading rather than clarifying.

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, when not to use it, or how it relates to sibling tools such as dt-codereview-check-tool and dt-codereview-tpl-tool. The only usage-like statement is "No setup, works in any LLM chatbot," which does not help an agent choose 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?

No annotations are provided, so the description carries the full disclosure burden, and it delivers almost nothing: no statement of what the tool returns, whether it is read-only, whether it produces a filled template versus instructions, or any cost/latency profile. The '30-day support promise' and 'prompt kit' language is commercial boilerplate rather than behavioral information.

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?

Three sentences, but the front-loaded sentence is the least relevant one and the closing sentence is pure sales filler. The only substantive clause is buried in the middle, so the structure works against quick comprehension.

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-required-parameter template generator with no output schema, the description should explain what the template contains and what the agent gets back; it does neither. The unrelated restaurant/brand framing leaves the agent uncertain whether this tool is even in the right domain.

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

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

Schema coverage is 100% for the single optional 'query' parameter, so the schema already does the work and baseline 3 applies. The description adds no meaning about how the query scopes the generated template, but nothing is missing that would block a correct call.

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 'restaurant & brand intelligence pack,' which is unrelated to a code-review template tool and actively misleads an agent. The operational sentence, 'generate a ready-to-use codereview tpl tool workflow,' essentially restates the tool name without defining what a 'tpl' output is or how it differs from the sibling gen/check 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?

There is no when-to-use, when-not-to-use, or sibling routing guidance at all. With dt-codereview-check-tool and dt-codereview-gen-tool present in the same family, an agent has no basis to pick this template tool over them; 'no prompt engineering needed' is marketing, not selection criteria.

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

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

With no annotations, the description carries the full behavioral burden and mostly fails it: it never states whether the check mutates anything, what the response contains (issues list? score? rewritten doc?), or what happens when the optional query is omitted. The one concrete disclosure — that the deliverable is a prompt pack containing a prompt, usage guide, and edge-case handling — is partially useful but also confusing, since it frames a 'check' operation as content delivery rather than analysis.

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?

Only three sentences, but the first is a slogan and the third describes packaged collateral rather than the tool's action, so the space is spent on promotion instead of purpose. The actual function is never front-loaded, leaving the reader to infer it from the name.

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 check/validation tool with no output schema and no annotations, the description should at minimum say what is validated and what a result looks like; it says neither. The audience claim about restaurant and brand teams also conflicts with the developer-oriented dt-api/dt-arch/dt-codereview sibling family, which may misroute the agent entirely.

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 is a single optional parameter whose schema description coverage is 100%, so the schema already explains that 'query' optionally scopes the analysis. Baseline 3 applies; the description adds no further meaning about how a query changes the check.

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 largely restates the tool name ("doc check tool prompt pack") without saying what is actually checked, what a check produces, or what kind of document it applies to. The lead sentence "Turn expertise into output" is a marketing slogan rather than a verb+resource statement, and nothing distinguishes this from dt-doc-gen-tool or dt-doc-tpl-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 guidance at all: no prerequisites, no trigger conditions, and no mention of the obvious alternatives dt-doc-gen-tool and dt-doc-tpl-tool that sit next to it. The only audience cue ("restaurant and brand teams") is a target market, not a selection rule.

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 behavioral burden, and it discloses almost nothing: not whether the tool produces a document, a prompt pack, or a checklist, not whether it needs prior context, and not what the result looks like. The only behavioral hint, 'copy-paste prompt pack with examples', is ambiguous and arguably conflicts with the name's implication of generating a doc. For an unannotated generation tool this is a significant gap.

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

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

Only two sentences, but both are marketing framing rather than operational information. The space is spent on audience ('marketing and operations leaders') and benefit ('repeatable workflow') instead of the resource, the output, or the sibling relationship, so the front-loaded content is the least useful content.

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

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

With one optional parameter, no output schema and no annotations, the description should define what a 'doc' is here, roughly what comes back, and how it differs from the check and tpl siblings. None of that is present, leaving an agent unable to predict the return value or choose correctly within a very crowded namespace.

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

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

Schema description coverage is 100% for the single optional 'query' parameter, so the schema already explains its meaning and the baseline of 3 applies. The description adds no scoping guidance (e.g., what a useful query would contain, or what happens when it is omitted), so it neither helps nor harms here.

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 restates the name ('doc gen tool') and pads it with marketing adjectives ('production-ready', 'for marketing and operations leaders') without saying what is actually generated, in what format, or for what document type. It never distinguishes itself from the obvious siblings dt-doc-check-tool and dt-doc-tpl-tool, nor from the parallel dt-api/dt-arch/dt-codereview families. The phrase 'delivered as a copy-paste prompt pack with examples' even muddies whether this generates a document or hands back a prompt template.

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 at all. Nothing states when to pick this over dt-doc-check-tool (validation) or dt-doc-tpl-tool (templating), which is the single most important routing decision for an agent facing this cluster. 'A workflow you can run today' is promotional copy, not a usage condition.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It never explains what the tool returns, whether it generates content or just returns a static prompt, whether the query parameter influences the output, or what format the playbook takes. The only behavioral hint is the no-setup claim, which is thin.

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 has three sentences, and the first one, 'Save hours every week', is pure sales copy that adds no information. 'Doc tpl tool toolkit' is redundant padding, and the remaining content is vague marketing language rather than a front-loaded explanation of what the tool does.

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 whose only distinguishing job is delivering a template or prompt artifact, the description fails to say what the artifact contains, how it is returned, or how the query parameter scopes it. The lack of annotations and lack of output schema make this omission more serious.

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 one optional parameter and schema description coverage is 100%, so the schema already documents what 'query' does. The description adds no meaning beyond that, but because the schema does the work, the baseline score of 3 applies.

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

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

The name is only restated as 'doc tpl tool toolkit', and the actual purpose ('prompt and operating playbook for restaurant/brand growth') is buried in marketing copy. There is no specific verb or resource, and nothing distinguishes it from dt-doc-gen-tool or dt-doc-check-tool, which appear in the sibling list.

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

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

The description gives no indication of when to use this tool, when to choose it over dt-doc-check-tool or dt-doc-gen-tool, or what the optional query parameter does. 'No setup, works in any LLM chatbot' is not usage 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?

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It effectively communicates that the tool is a 'full prompt kit with step-by-step instructions' and includes a '30-day support promise,' which are vital behavioral/service characteristics for a kit 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 highly concise and impactful, using only two sentences to cover the core value proposition, usage ease, and delivery method. All important elements are front-loaded for immediate agent understanding.

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

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

Given that the tool delivers a complex artifact (a workflow/kit) rather than simple text, the description is highly complete. It clearly defines the nature of the output and the level of support, making it sufficient for an agent to invoke it with confidence.

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 coverage is 100%, meaning the schema fully documents the single 'query' parameter. The description does not add semantic meaning beyond what is already provided by the formal 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 defines the tool as an 'AI-powered restaurant & brand intelligence pack' that generates an 'adcopy kit workflow.' It successfully categorizes the output as a complete 'kit,' helping the agent distinguish it from tools that might just generate a single piece of copy or content.

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 good guidance by stating that 'no prompt engineering' is needed to use the tool. However, it lacks explicit instructions on when this specific workflow-based tool should be used versus a simpler, single-copy-generating sibling 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?

No annotations are provided, so the description must carry the behavioral burden. It says the pack includes a prompt, usage guide, and edge-case handling, but it does not explain what the tool actually returns, whether the query scopes output, or any operational constraints.

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

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

The description is short and not padded, but it leads with the vague marketing phrase 'Turn expertise into output' rather than a concrete statement of purpose. It is concise without being well front-loaded 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?

The tool is structurally simple, but the description still leaves the core concept of an 'avoid kit' undefined. With no output schema and no annotations, the description should clarify what the tool produces and how the optional query changes it; it does not.

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

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

Schema coverage is 100%, and the single optional query parameter is already documented as scoping the analysis. The description adds no parameter semantics beyond the schema, so the baseline score of 3 is appropriate.

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

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

The description restates the tool name as an 'avoid kit prompt pack' and adds generic marketing language about helping restaurant and brand teams produce professional results. It never states a specific verb or resource, nor does it explain what 'avoid' means or how this kit differs from the many sibling kit tools.

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

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

It names an audience but gives no when-to-use guidance, no exclusions, and no alternatives among the dozens of related dy-* kits. An agent cannot infer when this tool is preferable to dy-adcopy-kit, dy-checklist-kit, or another avoid-kit variant.

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 supplied, so the description carries the full behavioral burden. It does disclose one useful fact - the deliverable is a "copy-paste prompt pack with examples" rather than finished content - but says nothing about what the tool returns structurally, whether it is stateless, or how much of the kit is produced per call.

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

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

Two short sentences with no repetition, but the leading clause is marketing filler ("production-ready", "you can run today") while the substantive information - what a case kit is - is absent. It is brief without being front-loaded with useful content.

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

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

For a simple one-optional-parameter prompt-pack tool with no output schema, the description is almost sufficient, but the missing platform identity (Douyin) and the absence of any distinction from the five identical-pattern case-kit siblings leave a real gap in how an agent picks 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?

Schema coverage is 100% and the single optional "query" parameter is documented as "Optional user query to scope the analysis." The description adds nothing about how the query influences the kit (topic, industry, platform), so baseline 3 applies.

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

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

"Case kit system" is a label rather than a verb-plus-resource statement; it never says what the tool actually produces (case-study copy, templates, or a prompt pack for Douyin-style content). With four direct siblings (gzh-case-kit, sph-case-kit, xhs-case-kit, zh-case-kit) plus dozens of other dy-* kits, the description gives an agent no basis to identify this as the Douyin case-study variant.

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

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

The only contextual cue is an audience ("marketing and operations leaders"); there is no statement of when to choose this over the gzh/sph/xhs/zh case-kit siblings. No prerequisites, no exclusions, no trigger conditions are given.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states 'No setup, works in any LLM chatbot' but does not explain what the tool returns (e.g., a prompt, a checklist, or an analysis), whether it has side effects, or what the query parameter controls.

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 not front-loaded with useful information. The first sentence is pure fluff ('Save hours every week'), and the second contains redundant phrasing ('checklist kit toolkit'). It does not clearly state the tool's function or how to invoke it effectively.

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 optional parameter and no output schema, the description should still clarify the tool's output format and the parameter's role. It fails to do so, instead offering generic marketing claims that leave the agent uncertain about what to expect from a call.

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

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

The input schema has 100% description coverage for the single optional parameter 'query', so the schema itself defines its meaning. The description adds no additional semantics beyond the schema, which is acceptable given the high coverage.

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

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

The description uses marketing language ('Save hours every week', 'battle-tested prompt and operating playbook') and redundant phrasing ('checklist kit toolkit') without clearly stating what the tool does. It mentions 'restaurant/brand growth' but does not indicate a specific platform (e.g., Douyin) or distinguish it from numerous similar sibling tools like gzh-checklist-kit or xhs-checklist-kit.

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

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

The description provides no guidance on when to use this tool versus alternatives. It does not mention any prerequisites, exclusions, or sibling tools, leaving the agent with no basis for selecting this over the many other checklist kits available.

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 supplied, so the description carries the full disclosure burden, yet it spends its words on marketing ("30-day support promise", "ships as a full prompt kit") rather than behavior. It never states whether the tool is a read-only generator, what artifact it returns, or how the optional query influences output, and the "restaurant & brand intelligence" framing conflicts with a content-column workflow.

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?

Three sentences, but the lead sentence describes an unrelated restaurant/brand intelligence product and the closing sentence promotes a support promise, neither of which helps an agent call the tool. Only the middle clause carries any operational meaning, so the text is padded rather than 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 zero-annotation, no-output-schema tool with one optional parameter, the description should establish what the generated kit contains and what the agent receives back. Instead it promises "step-by-step instructions" without describing them, leaving the agent unable to predict the result of an 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?

There is a single optional parameter with 100% schema description coverage ("Optional user query to scope the analysis"), so the schema already does the explanatory work. The description adds nothing about what scoping means here or what happens when query is omitted, leaving the baseline score appropriate.

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

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

The core claim, "generate a ready-to-use column kit workflow," gestures at a content-generation tool, but "column kit" is undefined jargon and the opening line ("restaurant & brand intelligence pack") describes a different product entirely, which actively misdirects. With four near-identical siblings (gzh-column-kit, sph-column-kit, xhs-column-kit, zh-column-kit) in the catalog, the description does nothing to distinguish this Douyin variant from them.

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 at all: no trigger conditions, no prerequisites, and no mention of the sibling column-kit tools an agent would need to choose between. "No prompt engineering needed" is a sales claim, not a selection rule, so an agent has no basis for picking this tool over gzh-/sph-/xhs-/zh-column-kit.

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

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

With no annotations, the description carries the full burden. It does disclose the delivered payload ('complete prompt, usage guide, and edge-case handling'), which is genuinely useful, but says nothing about output format, whether it stores state, or what the returned content looks like beyond that packaging list.

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?

Three sentences of promotional copy, with 'in minutes' and 'Turn expertise into output' as pure filler. The one informational item, the contents list, is buried last instead of 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 tool with no annotations and no output schema, the definition should at minimum pin down the platform (Douyin), the artifact produced, and how it differs from the sibling comment kits. None of that is present, so an agent cannot make a confident selection decision from this text alone.

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 is a single optional parameter with 100% schema description coverage, so the schema already explains 'query'. The description adds no meaning about how the query scopes the generated kit, which is the baseline-3 case rather than a 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 restates the name's promise ('comment kit prompt pack') and adds only marketing language ('Turn expertise into output', 'professional results in minutes'). It never states a concrete action such as generating Douyin comment copy, seed comments, or reply banks, and never distinguishes dy-comment-kit from the many sibling comment kits (gzh-comment-kit, xhs-comment-kit, sph-comment-kit, zh-comment-kit).

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

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

The only guidance is an audience hint ('restaurant and brand teams'), with no statement of when to choose this tool, when not to, or which sibling handles the same job on another platform. An agent facing five near-identical comment kits gets no routing signal at all.

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 supplied, so the description carries the full behavioral burden. It discloses only that the deliverable is a "copy-paste prompt pack with examples" — it says nothing about what the tool returns (text template? structured workflow?), whether the comparison targets a specific platform, or any side effects. For a tool with zero annotation coverage this is thin.

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

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

Two sentences, so not bloated, but a large share of the text is filler ("production-ready", "run today", "for marketing and operations leaders"). The most useful fact — that the output is a copy-paste prompt pack with examples — is buried at the end rather than front-loaded.

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

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

The tool is simple (one optional param, no output schema, no annotations), but the description still omits what the comparison covers, which platform/format the kit targets, and what the agent gets back. Given the enormous sibling set of near-identical *-compare-kit tools, an agent cannot safely choose this one from the definition alone.

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?

Only one optional parameter exists and its schema description is fully covered ("Optional user query to scope the analysis"), so the schema already does the work. The description adds no hint about what the query should contain or how scoping affects the generated kit, but the baseline for full schema coverage is 3.

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

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

The description restates the name ("compare kit system") and adds marketing framing ("production-ready", "for marketing and operations leaders") without stating what the tool actually produces or what is being compared. It never mentions Douyin/短视频 platform, so nothing distinguishes it from the many sibling compare kits (xhs-compare-kit, gzh-compare-kit, sph-compare-kit, zh-compare-kit). The only concrete detail is the delivery form ("copy-paste prompt pack with examples"), which hints at output type but not purpose.

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 platform-specific compare kits that appear throughout the sibling list, nor any prerequisites or exclusions. "You can run today" is promotional rather than operational. An agent must guess the platform and trigger conditions entirely.

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 supplied, so the description carries the full burden, yet it does not say whether the tool returns a static prompt template or analyzes the query and generates tailored output. It does not describe output format, length, or how the optional query affects results; "save hours every week" is promotional rather than behavioral 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 text is short (three sentences), but it leads with an unearned benefit claim ("Save hours every week") instead of the purpose, and the middle sentence is heavily padded with marketing adjectives ("battle-tested," "operating playbook"). The actual differentiating information is deferred or absent.

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 that appears to return a reusable prompt/playbook artifact, the description omits what the agent will actually get back, how the optional query is used, and how to choose this among ~30 sibling compete kits. The lack of annotations and output schema makes those gaps more costly, not less.

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

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

Schema description coverage is 100% and the single optional "query" parameter is documented as "Optional user query to scope the analysis," so the baseline of 3 applies. The description adds nothing about how a query changes the returned playbook or what happens when it is omitted.

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 says it provides "a battle-tested prompt and operating playbook for restaurant/brand growth," which hints at an artifact type, but there is no specific verb and "compete kit toolkit" essentially restates the name. It never clarifies what "compete" means here (competitor teardown? comparison content?) nor distinguishes this from the many near-identical siblings such as xhs-compete-kit, gzh-compete-kit, sph-compete-kit, and zh-compete-kit, and the "restaurant/brand" framing conflicts with the dy- (Douyin) naming.

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?

"Works in any LLM chatbot" and "No setup" describe delivery mechanics, not when to choose this tool. There is no statement of when to use it versus the dozens of sibling compete-kit variants, no prerequisites, and no exclusions.

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

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

No annotations are supplied, so the description carries the full behavioral burden, and it discloses almost nothing beyond sales language. It does not say whether the tool returns a prompt template, generated copy, or instructions; whether output is platform-specific; or what the '30-day support promise' means operationally for the caller.

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?

Three sentences of promotional copy ('Instantly generate', 'no prompt engineering needed', '30-day support promise') with the useful information — platform and output form — absent. The front-loaded framing as an 'intelligence pack' actively misdirects rather than orienting the 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 one-parameter, no-annotation, no-output-schema tool, the description should at minimum state what comes back and how it differs from sibling kits. Instead it leaves the return artifact undefined and spends its length on purchase-style assurances, so an agent cannot confidently call it or route to it.

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 is a single optional 'query' parameter with 100% schema description coverage, so the schema already explains its meaning and the description adds nothing about how scoping affects the result. With schema coverage this high, the baseline of 3 applies even though the description is silent on the 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 calls itself an 'AI-powered restaurant & brand intelligence pack' and then says it will 'generate a ready-to-use cover kit workflow', which is a vague restatement of the name rather than a specific verb+resource. It never says what a 'cover kit' actually contains (cover copy? image prompts? layout template?) or that it is for Douyin, despite dozens of near-identical siblings such as gzh-cover-kit, xhs-cover-kit, sph-cover-kit and zh-cover-kit that the agent must choose between.

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 pick this tool over the other cover-kit variants or over dy-adcopy-kit/dy-topic-kit, and no prerequisites or exclusions are stated. The only usage-adjacent sentence, 'no prompt engineering needed', is a marketing claim, not a selection condition.

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

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

No annotations are provided, so the description must carry the full behavioral burden. It says the pack includes a prompt, usage guide, and edge-case handling, but does not describe what the tool actually returns, whether it performs generation or just retrieves a template, or any operational constraints.

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

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

The description is short but front-loads a slogan ("Turn expertise into output") and promotional claims ("professional results in minutes") rather than useful task information. Several sentences do not earn their place because they add no concrete guidance.

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

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

With no annotations and no output schema, the description should explain the tool's core behavior and return expectations. Instead it gives generic prompt-pack marketing and omits the platform-specific purpose implied by the dy- prefix, leaving the definition incomplete for an agent to use confidently.

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

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

There is one optional query parameter, and schema description coverage is 100%, so the schema already documents it. The description adds no additional meaning about how the query scopes the analysis, so the baseline score of 3 is appropriate.

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

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

The description mostly restates the name with marketing phrasing: "explain kit prompt pack" and "turn expertise into output." It does not clarify what platform or task this is for (e.g., Douyin explainer content) or distinguish it from siblings such as gzh-explain-kit, xhs-explain-kit, sph-explain-kit, and zh-explain-kit.

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 names a broad audience ("restaurant and brand teams") but gives no when-to-use guidance, prerequisites, or alternatives. There is no indication of when an agent should choose dy-explain-kit over the many other explain-kit siblings.

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

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

No annotations exist, so the description carries the full behavioral burden. It does disclose the deliverable shape (a copy-paste prompt pack with examples), which is genuinely useful context, but it says nothing about side effects, permissions, whether output is static or tailored, or the scope/nature of the returned content.

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

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

Two sentences, so it is short and front-loaded, but the opening is spent on empty qualifiers ("production-ready", "you can run today") instead of identifying what the kit contains or which platform it serves. Space that could carry differentiating detail is wasted on marketing language.

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 a single optional parameter, no output schema, and low complexity, the description is nearly sufficient: it states the deliverable format and the target audience. The one real omission is platform/domain identity, which for this heavily duplicated sibling family is significant but does not make the definition unusable on its own.

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 a single optional 'query' parameter at 100% description coverage, so the baseline is 3. The description does not mention the query parameter or explain how it scopes the result, but the schema already handles that meaning adequately.

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

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

"Guide kit system" largely restates the tool name, and the rest of the description describes the audience (marketing and operations leaders) and the wrapper format (copy-paste prompt pack) rather than the actual job the tool performs. It never mentions the platform ('dy' = Douyin) or the subject of the guide, so an agent cannot tell it apart from the dozens of near-identical siblings such as gzh-guide-kit, xhs-guide-kit, sph-guide-kit and zh-guide-kit.

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, when-not-to-use, or alternative-tool guidance. Phrases like "you can run today" and "production-ready" are promotional, not selection criteria, so nothing tells an agent to pick this over the many sibling *-guide-kit tools.

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

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

With no annotations, the description carries the full burden. It does imply the tool returns a prompt plus playbook rather than generating finished content, which is useful, but it never states whether it is a pure read/retrieval operation, what the response looks like, or how the returned playbook is meant to be applied. 'No setup, works in any LLM chatbot' is compatibility marketing rather than behavioral 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?

It is short, but two of the three sentences ('Save hours every week,' 'No setup, works in any LLM chatbot') are advertising copy that forwards no information an agent can act on, and the marketing hook is front-loaded ahead of the actual 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?

For a single-parameter prompt-kit tool with no annotations and no output schema, the description should explain what is returned and how to use it. It leaves the agent guessing about the deliverable's shape and the platform context (Douyin), which is the one thing that distinguishes it from the surrounding family of listicle kits.

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

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

Schema coverage is 100% for the single optional 'query' parameter, so the schema already documents it. The description adds nothing about how the query scopes the returned prompt, so the baseline of 3 applies.

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

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

The description says it delivers a 'battle-tested prompt and operating playbook,' which hints at the deliverable, but never states a verb or resource such as 'returns the Douyin listicle content prompt template.' The 'dy' prefix, i.e. Douyin, is never unpacked, and the scope claim 'restaurant/brand growth' does not match a listicle-format content kit, so the agent cannot confidently tell what the tool produces versus siblings like xhs-listicle-kit or gzh-listicle-kit.

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 and no mention of alternatives. The only framing is 'for restaurant/brand growth,' which tells the user a domain but not the condition that should select this tool over the many other *-listicle-kit 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?

With no annotations, the description carries the full behavioral burden. It hints that the output is "a full prompt kit with step-by-step instructions," but says nothing about whether generation is synchronous, what the kit contains, cost/limits, or side effects; the "30-day support promise" is sales copy, not 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?

Only three short sentences, so it is compact and front-loaded. However, the first sentence misdirects toward restaurant/brand intelligence and the last is pure promotional filler about a support promise, so not every sentence earns its place.

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

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

The tool itself is simple (one optional parameter, no output schema), yet the description leaves the agent unsure which platform's live kit this produces and what a "live kit" actually is, while spending space on marketing claims instead.

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

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

Schema coverage is 100% and the single `query` parameter is documented with an optional scope hint, so the schema does the work. The description adds no meaning about how the query scopes the generated kit, which is the baseline-3 outcome.

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?

"Instantly generate a ready-to-use live kit workflow" gives a verb and a resource, but "live kit" is the name restated and the leading sentence calls it a "restaurant & brand intelligence pack," which has nothing to do with a Douyin livestream kit. It never says the platform (dy) or how it differs from the ~30 sibling *-live-kit tools (gzh-live-kit, xhs-live-kit, sph-live-kit, zh-live-kit), so an agent cannot route on this text.

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 condition, no prerequisites, and no named alternative among the many sibling live-kit variants. The only usage-adjacent claim is "no prompt engineering needed," which is marketing rather than 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?

No annotations are provided, so the description carries the full behavioral burden. It only says the pack includes a prompt, usage guide, and edge-case handling; it does not explain whether the tool returns static content, generates something based on the query, what limitations exist, or whether the operation is read-only.

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 not repetitive, but the opening line "Turn expertise into output" is vague marketing language that does not help tool selection. The useful information about the prompt pack and included materials is present but not front-loaded or organized around invocation decisions.

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?