中文 AI 可引用性（GEO/AEO）体检

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

Beyond the readOnlyHint/idempotentHint annotations, the description discloses that a single fetch performs six deterministic checks, includes real interception testing of Chinese crawler matrices, intentionally does not call an LLM, and reports according to scoring_version. These are useful behavioral traits not inferable from annotations alone.

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

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

Compact, well-structured paragraphs front-load the core action, then layers, output, use cases, and caveats. No fluff; every clause adds operational detail.

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 complex audit tool, the description covers the six check layers, the 0-100/A-F output, evidence and priority-fix list, compare_with mode, determinism/no-LLM behavior, and scoring-version reference. The only notable gaps are include_raw semantics and sibling-tool routing, which keep it from being fully complete.

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

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

Schema description coverage is 0%, so the description carries the burden. It gives meaning to url (the site/page being audited) and compare_with ('传 compare_with 可做竞品对比（逐层与逐项差异）'), but leaves include_raw entirely undocumented, so an agent cannot tell whether raw means raw evidence, raw HTML, or raw response scores.

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

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

The description opens with a concrete verb-resource pair—'审计一个网站/页面能否被中文 AI 搜索…抓取、解析与引用'—and enumerates six specific check layers plus the score/grade output. This clearly differentiates it from inspection-style siblings like plan_fixes and score_visibility, even though those names aren't explicitly mentioned.

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?

States explicit use scenarios ('客户站点体检、上线前自检') and describes the compare_with competitive-comparison mode. It doesn't name sibling tools as alternatives or list when-not-to-use cases, so it stops at clear context without exclusions.

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

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds valuable behavioral detail beyond that: passing url causes an audit-then-plan flow, and the output is a prioritized list with rationale, remediation, and acceptance criteria. 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: purpose first, then usage modes, then return value. Every sentence carries useful information, with 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?

Given the simple schema, rich annotations, and existing output schema, the description covers the main operational aspects: both usage modes and the shape of the returned plan. The main gap is the undocumented top parameter and the lack of explicit guidance on what happens if both fail_ids and url are provided or neither is provided.

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 explains fail_ids with a concrete format example and url with its audit behavior, but it does not explain the top parameter at all, leaving its meaning and effect on output unclear.

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

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

The description opens with a specific verb-object statement—'按缺口生成优先修复计划'—clearly identifying the tool as generating a prioritized fix plan. It also distinguishes its two invocation modes (fail_ids vs. url), which helps an agent tell it apart from auditing, probing, and scoring sibling tools.

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

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

The description gives clear context for when to use the tool and explicitly explains two usage routes: pass fail_ids directly, or pass url which triggers an audit first. It does not explicitly name sibling tools as alternatives or state when not to use it, but the usage conditions are clear enough for an agent to select it.

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

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

Annotations already declare readOnly, idempotent, non-destructive behavior, so the description does not need to repeat safety traits. It adds meaningful behavioral context beyond annotations: the tool analyzes a question's source-pool occupation, classifies platforms, detects brand/domain presence, and returns competitor domains and suggested actions. No annotation contradiction exists.

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

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

The description is well-organized into purpose, rationale, return contents, and parameter semantics. Every section earns its place: the principle explains why the tool matters, the return list sets expectations, and the parameter notes use concrete examples. The first sentence front-loads the core 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 3-parameter read-only probe tool with an output schema, the description is complete: it covers what the tool does, why it exists, what it returns, and how to fill each parameter. The output schema handles structural details, so the description does not need to enumerate return types further.

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

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

With schema description coverage at 0%, the description carries the full burden of explaining parameters. It explicitly defines question as the user's real wording, brand as the brand term with an example, and domain as the user's domain with an example. This converts opaque parameter names into actionable instructions.

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 action—probe the actual source-pool distribution for a Chinese question—and the resource (信源池), plus the secondary judgment of whether the brand/domain appears. This clearly differentiates it from the sibling tools (audit_cn_citability, plan_fixes, score_visibility) which sound like audit/fix/scoring utilities.

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

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

The description gives clear context: use this when you need to know whether a brand/domain has already entered the retrieval pool for a real question, and explains the underlying reason (AI answers only cite sources already in the pool). It does not explicitly name alternatives or when-not-to-use conditions, but the purpose context is strong enough to guide selection.

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

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

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds valuable behavioral detail beyond that: it produces per-question breakdowns, original excerpts for client self-verification, live URL accessibility checks when verify_citations=True, and extracted numeric claims marked as unverified. This is consistent with the annotations and discloses important external-check behavior.

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

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

The description is long but dense and every sentence earns its place: overview, sample JSON template, methodology note, output contents, and optional flag behaviors. The critical invocation details are front-loaded, and the inline JSON example is an efficient substitute for missing schema descriptions.

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

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

Given the complexity of a nested JSON-string input and zero schema field descriptions, the description is complete enough for correct invocation. It covers required input structure, suggested sampling frequency, output composition, and optional verification modes. An output schema exists, so the description is not obligated to enumerate every return value.

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%, but the description fully compensates by embedding the exact JSON structure for the samples string, including all relevant fields, enums for role/sentiment/facts, arrays, and the date format. It also explains the two optional boolean parameters and their effects. This is strong parameter-level guidance where the schema itself provides none.

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 core operation: '按五大核心指标 + 语义角色分权，计算 AI 搜索可见度并输出可自验的测量报告'. This is a specific verb and resource, and the tool is clearly a measurement/reporting tool rather than a fixer or auditor. However, it does not explicitly contrast itself with sibling tools such as audit_cn_citability or plan_fixes, so it misses full 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 gives concrete usage guidance: samples must be a JSON string, multiple rounds are recommended ('每题建议多轮（同一问题重复 7-8 次）'), and it explains what happens when verify_citations or extract_claims are enabled. It does not explicitly state when this tool should be preferred over alternatives or when it should not be used, so it falls just short of a 5.

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