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

With no annotations and no output schema, the description carries the full disclosure burden and largely meets it: it states coverage (all 18 niches), the exact metrics returned, and the qualifying scope ('over tested hypotheses'). It does not explicitly confirm read-only behavior, but the 'list' framing and fixed small cardinality make the safe-read profile unambiguous.

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?

Three short sentences, correctly front-loaded with what is returned, then the usage cue, then the attribution requirement. Every clause carries information; nothing is padding.

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 must convey the return shape, and it does so precisely (counts: hypotheses/tested/worked; medians: cost per subscriber, cost per lead, USD). It also supplies the citation/license obligation an agent needs, leaving nothing material missing.

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?

The tool takes zero parameters, so the baseline is 4. There is nothing to document, and the description correctly does not invent parameters.

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-plus-resource ('All 18 niches of the Digital Experts hypotheses base') and enumerates exactly what each row contains (counts and medians). It is clear what the tool returns, but it never names or contrasts with the sibling niche_stats, which sounds like a closely related niche-scoped tool.

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?

'Start here to pick a niche' gives an explicit entry-point instruction that tells the agent when this tool is the right first call. It does not name alternatives or state when not to use it, so exclusion guidance is absent, but the usage context is clear.

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