AI 客服国标合规知识库 (GB/T 47746—2026)

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is fully covered. The description adds the behavioral detail that the tool returns the full content including specific sections, which is useful. However, it doesn't disclose anything beyond that, such as error behavior for invalid slugs or whether the response is structured. With annotations covering the safety profile, a 3 is appropriate.

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

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

The description is compact and front-loaded: the first sentence states the purpose and the return contents, and the Args section is a single line that adds the key usage hint. Every sentence earns its place; no fluff.

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

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

For a single-parameter read tool with an output schema present, the description is nearly complete. It explains what the tool returns, how to get the required parameter, and the annotations cover safety. The only missing piece is error handling for invalid slugs, but that is minor given the output schema and the explicit prerequisite guidance.

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 that slug is the Q&A identifier and tells the agent how to obtain it (via list_questions or search_answers). This adds meaning beyond the bare schema property name 'Slug'. The only minor gap is not specifying the format of the slug, but the guidance to get it from sibling tools is sufficient.

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

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

The description clearly states the tool's purpose: retrieving the full content of a Q&A entry by slug, listing the specific components (正文, 要点, 标准条款依据, 常见追问). It distinguishes itself from siblings by explicitly instructing the agent to first use list_questions or search_answers to obtain the slug, which differentiates it from listing/searching 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 explicitly tells the agent when to use this tool: after obtaining a slug from list_questions or search_answers. It also implies this is the retrieval tool for full details, as opposed to the listing/searching siblings. This is clear usage guidance with an explicit prerequisite.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds the 'all' scope and the cluster filter semantics, but does not mention pagination, ordering, or behavior when cluster is omitted. There is no contradiction with annotations.

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

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

The description is two sentences with no filler, front-loading the core purpose before parameter details. Every element earns its place.

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

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

For a simple list tool with one optional parameter, an output schema, and safety annotations, the description sufficiently covers purpose and the filter parameter. It does not explain when to prefer this over sibling tools or what 'all' means when cluster is omitted, but these are minor gaps given the low complexity.

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

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

Schema description coverage is 0%, so the description carries the full burden for the single parameter. It explains that cluster is optional, is used for filtering, and explicitly lists the allowed values ai-service-standard and enterprise-knowledge-base. This fully compensates for the schema's lack of 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 description states a specific verb and resource: it lists the title list of all compliance Q&A entries, and the cluster filter clarifies scope. It does not explicitly distinguish itself from siblings like search_answers or get_answer, but the listing action is clearly different from search/get operations. This is clear but lacks explicit sibling differentiation.

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

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

The description implies usage when the agent needs the full inventory of compliance Q&A titles, optionally filtered by cluster. It provides the exact allowed cluster values but gives no explicit guidance on when to choose list_questions over search_answers or get_answer. This is implied usage rather than explicit routing.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so safety is covered. The description adds useful behavioral context: results are ranked by relevance and contain brief answers rather than full text. No contradiction with annotations.

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

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

The description is compact and well-structured: a single front-loaded purpose sentence followed by a short Args block with examples. Every sentence earns its place; there is no filler or redundant restatement.

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

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

With only two simple parameters, annotations covering safety, and an output schema present, the description covers purpose, parameter semantics, and result form (most relevant items with brief answers). Nothing critical 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?

Schema description coverage is 0%, so the description carries the full burden. It fully documents both parameters: query is explained as the search term with three concrete examples, and top_k is defined as the number of results with a default of 5. This adds meaning well beyond the bare schema types.

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 a specific verb ('检索'/'search') and resource ('合规问答'/'compliance Q&A'), and states that it returns the most relevant results with brief answers. This clearly identifies what the tool does and implicitly distinguishes it from siblings like get_answer (single answer) and list_questions (list questions).

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 a clear usage context—keyword-based search of compliance Q&A—so an agent can infer when to invoke it. However, it does not explicitly name alternative tools or state when not to use it, so it lacks explicit exclusions/sibling routing.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds the content scope (the self-check checklist for the standard) but does not disclose additional runtime behavior such as return size or format, though an output schema exists to fill that gap.

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

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

The description is a single sentence with no filler, front-loading the exact resource and immediately stating its practical purpose. Every word 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 that the tool has no parameters, annotations cover read-only/idempotent behavior, and an output schema exists, the description provides everything an agent needs to select and call the tool correctly.

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

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

There are zero parameters, so no parameter documentation is needed. Per the rubric, a zero-parameter tool gets a baseline of 4, and the description adds relevant context about what is being retrieved.

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 a specific verb ('取') and a precise resource ('GB/T 47746-2026 的自查清单'), and clarifies the intended purpose: enterprises checking whether their own AI customer service meets the standard. This clearly distinguishes it from sibling tools like get_answer, list_questions, and search_answers.

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

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

The description explicitly states the use case: an enterprise checking its own AI customer-service compliance against the named standard. It does not name alternatives or exclusions, but the context is clear enough for an agent to decide when to invoke this zero-parameter retrieval tool.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds behavioral context by specifying exactly what kind of information is returned (dates, committee, length, core requirements), which is useful beyond the annotations.

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

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

The description is a single compact sentence that front-loads the action and resource, then lists the delivered metadata. Every element is informative 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?

With zero parameters, an output schema present, and annotations already covering the read-only/idempotent behavior, the description provides all necessary context. Nothing critical is missing for an agent to invoke the tool correctly.

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

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

The tool has zero parameters and the schema coverage is 100%, so there is nothing for the description to add about parameters. Baseline 4 applies because no parameter ambiguity exists.

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

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

The description names a specific verb ('获取', obtain) and a specific resource (GB/T 47746-2026 standard), and lists the exact metadata fields it returns. This makes the tool's purpose unambiguous and distinguishes it from the sibling Q&A/search 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 implies usage: call this tool when standard metadata for GB/T 47746-2026 is needed. However, it gives no explicit guidance on when not to use it or how it relates to siblings like get_answer or search_answers, so routing must be inferred.

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