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

Annotations flag destructive, open-world, and non-idempotent behavior. The description adds critical context: it costs SEO add-on credits (scales with prompts × engines × repeats), requires a top-up, and that repeats average out LLM non-determinism. It does not explain what 'destructive' entails beyond credit consumption, but it meaningfully supplements the 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?

Front-loads the core purpose and flows logically through usage, parameters, and cost. It is a dense single paragraph with emphatic ALL-CAPS that could be distracting, but every sentence contributes useful information and there is 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?

Given 7 parameters, an output schema, and annotations, the description is largely complete: it explains what the tool does, most parameters, credit cost, and engine options. It omits the refresh parameter and any authentication/connection prerequisites, but those are minor gaps against a strong overall treatment.

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 carry the parameter semantics. It covers prompts (3-8 real questions), brand, optional competitors and brand_domain, engines default (chat_gpt,perplexity with gemini add-on), and hints at repeats behavior. It leaves the 'refresh' parameter entirely unexplained, which is a notable gap.

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 ('runs') and resource ('your prompt set through real LLMs'), then names the exact measurements (brand mentions, citations, sources cited). It explicitly distinguishes itself from 'classic rank tracking', making its scope unmistakable.

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?

Clearly frames the use case (GEO visibility inside AI answers) and gives concrete prompt guidance ('3-8 real buyer/category questions, UNbranded works best'). It does not name alternative siblings like seo_geo_citations or state when not to use it, but the context is clear.

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