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

With no annotations, the description carries the full burden. It helpfully discloses the return payload (total score with a 20-point maximum and a release recommendation), but omits whether the assessment is read-only, whether the result is persisted or logged, and any auth or rate-limit behavior. Disclosure of outputs is good; side-effect transparency is absent.

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

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

A single dense sentence that front-loads what is being scored and what is returned before naming the use case. No filler or repetition.

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

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

No output schema and no annotations exist, so the description must cover behavior. It explains the output shape adequately, but leaves open what the caller does with the recommendation, whether scores persist, and whether the tool is pure function evaluation or records state — gaps for a pre-release gate tool.

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

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

Schema description coverage is 100%, and each parameter already carries a directional meaning ('higher = safer/more reversible'). The description only restates the four dimension names, adding no syntax or edge-case semantics beyond the schema, so the 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?

The description names the resource (A³ Law four-dimension scorecard) and specifies the dimensions and output (total score out of 20 plus a release recommendation). The verb is implicit rather than stated, and the 'A³ 法则' framing is jargon an agent must parse, but the tool's function is clearly ascertainable and distinguishable from audit/passport/compliance siblings.

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 states a clear condition for use: pre-release assessment of AI that creates/modifies other AI or high-impact actions. No explicit when-not or named alternatives are given, so it stops short of full routing guidance, but the triggering context is unambiguous.

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 a meaningful behavioral trait — this produces template artifacts rather than performing an assessment — which separates it from the audit/assessment siblings. It does not say whether anything is written to disk, how templates are returned, or whether generation is idempotent.

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

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

A single sentence with the verb and deliverable front-loaded, followed by the artifact enumeration and the purpose clause. The six-item list is long but each entry is substantive rather than filler, so it earns its space.

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

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

There is no output schema, so the description should cover what comes back, yet it never says whether the result is template text, a file, or a structured artifact set. It is adequate on what is generated but incomplete on the return contract for a generator 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?

There is only one optional parameter and schema description coverage is 100%, so the schema already fully documents system_name ('AI 系统/智能体名称'). The description adds no naming, format, or defaulting guidance beyond that, making the baseline 3 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?

States a specific verb (生成/generate) and resource (可审计证据工件模板) and enumerates the six concrete artifact types, which lets an agent understand the deliverable immediately. It does not, however, differentiate itself from the sibling compliance_checklist, which is the closest competing tool for governance artifacts.

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 framing '为 AI 系统准备可追溯审计证据链' implies the situation (preparing audit evidence for an AI system), but there is no explicit statement of when to use this versus compliance_checklist, a3_assess, or gov_scan, and no prerequisites or exclusions are given.

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

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

With no annotations, the description carries the full behavioral burden. It usefully discloses a scope limit ('非法律意见' / not legal advice), which is real behavioral context, but says nothing about whether the output is deterministic, how it is sourced, or what the returned checklist looks like.

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

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

A single dense sentence that front-loads the verb and resource, then lists the covered standards, then closes with the scope caveat. Every clause earns its place; no filler.

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

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

For a zero-parameter generator with no output schema, the description supplies the resource, the frameworks covered, the intended use case, and the not-legal-advice caveat. Adequate, though it does not indicate the output's shape or the basis for its recommendations.

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

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

The tool takes zero parameters, so per the baseline there is nothing for the description to disambiguate. No parameter semantics are required or missing.

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

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

States a specific verb (生成/generate) and resource (AI 治理合规清单), and names the concrete frameworks it covers (EU AI Act 2024/1689, GB/Z 185, ISO/IEC 42001, PIPL/GDPR). An agent can tell it apart from siblings like audit_evidence_chain or gov_scan, though the differentiation is by implied subject matter rather than explicit contrast.

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?

Implies usage context with '面向自主 agent 注册就绪的自评起点' (a self-assessment starting point for agent registration readiness), which hints at when it applies. However, it gives no explicit when-not guidance and does not name or route to any sibling tool as an alternative.

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

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

No annotations are provided, so the description carries the full burden. It discloses meaningful behavior: which pattern classes are detected, that the scan is text-based, and that a names-list hit triggers an alert. It does not state what the result looks like, whether it is read-only (implied), or any limits, leaving real gaps for a no-annotation 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?

A single compact sentence front-loads the tool's category and then lists detection targets followed by the optional parameter note. Nothing is wasted, though the dense enumeration of pattern classes makes it slightly harder to parse at a glance.

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

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

For a two-parameter, zero-required, no-annotation tool with no output schema, the description covers what gets scanned but says nothing about the shape of results or how findings are reported. That is adequate-but-incomplete: an agent knows what the tool detects but not what it receives back.

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

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

Schema description coverage is 100%, so both parameters are already documented in the schema. The description's note that names is a comma-separated list whose hits raise alerts merely restates the schema text and adds no new syntax or format detail, 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 gives a specific verb+resource (a governance/PII scan) and enumerates the exact detection targets: phone numbers, emails, local paths, suspected credentials, key-like strings, and missing copyright fingerprints. This is far more specific than a generic 'scan' label, though it never names a sibling tool to differentiate itself from compliance_checklist or audit_evidence_chain.

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

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

There is no explicit when-to-use, when-not-to-use, or alternative-tool guidance. The enumerated detection categories only weakly imply the appropriate context; an agent must infer that this is the tool for ad-hoc PII/governance text scanning rather than the structured compliance siblings.

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

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

No annotations are provided, so the description carries the full burden. It usefully discloses the return payload (three laws, three checks, normative sources with DOI/repo/official-site, domain papers), but says nothing about whether it is read-only, auth requirements, or rate limits.

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

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

A single dense, front-loaded sentence that leads with the endpoint's purpose. It is somewhat crammed with parenthetical lists, but every element earns its place and nothing is redundant.

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?

Both parameters are optional, no output schema and no annotations exist, so the description needs to do more. It adequately covers what is returned and the domain routing, but omits usage guidance and any behavioral/access context for a knowledge lookup.

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%, so baseline is 3, but the description adds real value by enumerating the domains the `domain` parameter can route to (金融/证据/政务/链上/驾驶/数据/低空/机器人/工业/基因/教育/医械), which the schema's '可选：直接指定域关键词' leaves open.

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

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

The description names a specific resource (LGD 全程治理论知识端点) and states concretely what it returns: 三律/三查/规范出处 with DOI/repo/official-site citations, plus topic-based routing to domain papers. However, it offers no differentiation from the sibling tools (a3_assess, audit_evidence_chain, gov_scan), which an agent must distinguish from this one.

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

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

The phrase '可按主题路由到对应域论文' describes a capability rather than guidance. There is no statement of when to use this tool versus a3_assess or the other siblings, and no when-not or prerequisite 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 does hint that the data is a local sample ledger ('本地台账示例'), which is useful context, but it never states that the operation is read-only, where the data comes from, or what the returned traceability fields contain.

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

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

It is a single front-loaded sentence with no filler, though the em-dash clause adds ambiguity rather than information, keeping it just short of ideal.

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?

Complexity is low (one optional parameter) and there is no output schema, so the description should explain what the lookup returns. It gestures at '溯源字段' but never enumerates them, leaving the agent guessing about the result shape.

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 'skill' parameter, so the schema already documents the identifier and its example format. The description's '技能/智能体身份码' wording essentially restates the schema, adding no syntax or constraint beyond it, so the 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?

The description names a verb and resource (查询技能/智能体身份码与溯源), so an agent knows it looks up an identity code and its provenance. However, the trailing clause '本地台账示例/展示…格式与字段' frames it as a demonstration of a format rather than a concrete lookup, leaving the actual output purpose ambiguous, and it does not distinguish itself from siblings like audit_evidence_chain or gov_scan.

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 versus the sibling tools (audit_evidence_chain, gov_scan, lgd_knowledge) or any prerequisite context. Usage can only be inferred from the word 查询.

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