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

With no annotations provided, the description carries the full burden and does disclose that the tool returns an 'LLM-grounded answer with evidence and ranked actions.' However, it omits relevant behavioral caveats such as possible nondeterminism, latency, or whether the answer reflects live or cached analytics data, leaving clear gaps.

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 sentences with the main purpose front-loaded, followed by concrete examples and a brief statement of what is returned. Every sentence earns its place and there is no redundant text.

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 tool's moderate complexity, the schema fully documents inputs, sibling tools provide context, and the description explains the output type at a useful level. A minor gap is not mentioning default lookback or limitations, but the absence of an output schema is reasonably compensated by the 'evidence and ranked actions' phrase.

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% and each parameter (question, days, site) already has a clear description. The tool description adds no extra semantic detail beyond the schema, so the baseline score of 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?

The description uses a specific verb+resource pairing ('Ask... about your site visitors') and gives concrete example queries, making it unmistakable what the tool does. It also distinguishes itself from structured siblings like eyepup_log and eyepup_visitors_hot by emphasizing free-text natural-language questions.

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 says 'Use this when...' and provides example user phrasings, giving clear context for when to invoke it. It doesn't explicitly list exclusions or alternative tools, but the free-text scope strongly implies it is the natural-language counterpart to the more structured sibling tools.

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