aiethos_check_llm_citations

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.