multi_llm_citation_check

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

With no annotations provided, the description carries full burden. It discloses the tool's behavior: it produces citation-share metrics and top competing domains per engine, based on user questions. It also positions the tool as a headline metric for GEO/AEO and suggests a diffing workflow, which adds behavioral context beyond a simple read operation.

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 paragraphs with front-loaded purpose: the first sentence states the core action. Every sentence adds value, with no fluff. It efficiently covers purpose, inputs, usage, and output nature.

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 presence of an output schema (not shown but indicated), the description does not need to detail return values. It provides complete context: inputs (questions, target_domain, engines), usage advice (top 20-50, weekly diff), and output nature (per-engine breakdown, top competing domains). It also explains the tool's strategic role as a headline GEO/AEO metric.

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 that 'questions' are 'your top 20-50 user questions' and 'target_domain' is the domain to check. 'engines' is implied by 'per-engine breakdown'. However, it does not specify constraints like question length limits, domain format, or the default behavior for engines (null meaning all).

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 specific verbs ('Citation-share metrics', 'feed it your top 20-50 user questions', 'get a per-engine breakdown') and clearly identifies the resource (target_domain) across configured LLMs. It distinguishes itself from siblings like 'perplexity_citation_check' (single engine) and 'multi_llm_query' (querying) by positioning itself as the 'headline GEO/AEO tool' and describing its 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?

The description explicitly recommends use cases: 'feed it your top 20-50 user questions' and suggests a cadence ('Run weekly and diff to track AI-search visibility over time'). It implies that for single-engine citation checks, one would use engine-specific tools (e.g., 'perplexity_citation_check'), but does not explicitly state when not to use this tool.

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