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

With no annotations, the description carries the full disclosure burden. It does add genuinely useful behavioral context by naming the three external LLM providers queried and the query class ('common industry queries'), but says nothing about latency, cost, rate limits, whether queries are live or cached, or determinism of results across calls.

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 core action front-loaded and a useful contrast clause ('or whether competitors dominate citations'). No filler, no repetition of the tool 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?

No output schema exists, so the description is the only place a caller learns what comes back. It implies a per-LLM citation verdict plus competitor comparison, but never states the shape of the response (per-model breakdown? a single verdict? scored ranking?) or run cost, which matters for a tool that fans out to three providers.

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 the baseline would be 3, but the description actually introduces mild ambiguity rather than added meaning: it says 'brand or domain' while the schema has only brand_name, and 'common industry queries' loosely gestures at industry_category without explaining how that value scopes the queries. It also arguably duplicates the schema's own examples rather than clarifying them.

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 (checks citations) and resource (brand/domain citation presence across named frontier LLMs: OpenAI, Gemini, Claude), which is far more concrete than the tool name alone. It falls short of a 5 because it never distinguishes itself from the sibling tools aiethos_geo_quickscan and aiethos_preview_ethostron_fix, which plausibly overlap in scope.

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?

Usage is implied by the phrase 'for common industry queries' and the competitor-domination framing, so an agent can infer this is a brand-visibility audit. There is no explicit when-to-use, no when-not-to-use, and no routing to the quickscan sibling, which is the most likely 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 disclosure burden. It implies a passive read-only audit and lists what gets analyzed, but says nothing about whether the scan fetches live pages, how long it takes, rate limits, or what the result contains — leaving the agent to guess at cost and side effects.

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 front-loaded sentences with the action first and the analysis scope second; almost every clause carries information. The trailing "for LLMs like Perplexity, ChatGPT, and Google AI Overviews" is slightly padded but does convey the evaluation targets.

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 output schema, the description should say roughly what an audit returns (scores, findings, gap list). It names the analysis dimensions but not the deliverable, so an agent knows what the tool inspects but not what it will get 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 coverage is 100%, so both parameters are already fully documented in the schema, including accepted URL/domain formats and the optional brand_name. The description adds no parameter-level meaning beyond that, which is the expected baseline rather than a strength.

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 and resource ("Performs a fast GEO audit on a domain or URL") and enumerates the four analysis dimensions, so the purpose is unmistakable. It does not, however, distinguish itself from the sibling aiethos_check_llm_citations, even though "identifies top citation gaps" overlaps that tool's apparent domain.

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?

"Fast" implies a lightweight variant exists, but the description never states when to choose this over aiethos_check_llm_citations or aiethos_preview_ethostron_fix, and gives no prerequisites or exclusions. Usage must be inferred from the tool name and the word "quickscan."

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 usefully signals that the output is a *sample* / preview artifact rather than a live page edit, and it enumerates what is produced (inverted-pyramid copy, Schema.org markup, evidence structure). It does not state whether the target URL is crawled, whether anything is persisted, or whether there are cost/rate constraints — real gaps for a generation tool.

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

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

The output contents are front-loaded in a single dense sentence, which is efficient, but 'The Taste Test' and 'EthosTron Live engine' are brand filler that consume the highest-attention position without telling the agent anything operational.

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?

Because there is no output schema, the description correctly spends its words enumerating the three returned artifacts, which is the right call. However, for a generation tool with zero annotations it omits the operational facts an agent needs before invoking: side effects, whether the URL is fetched, and how this fits after a citation check.

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%, including enum values and per-field explanations, so the description is not required to define parameters. It says nothing about url, gap_topic, or intent_category, so it adds no meaning beyond the schema. 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?

States a specific verb and resource: 'Synthesizes a sample authoritative Direct-Answer Content Block' using a named engine. That distinguishes it from the sibling check_llm_citations (diagnostic) and geo_quickscan (audit). The 'Taste Test' branding is decorative rather than informative, but the substantive clause carries the 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 statement of when to use this versus aiethos_check_llm_citations or aiethos_geo_quickscan, and no prerequisites such as whether the URL must already be scanned or whether an existing gap must be confirmed. The word 'preview' hints at a dry-run step but the description never says so explicitly.

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