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

Annotations already declare readOnlyHint, idempotentHint and destructiveHint=false, so the safety profile is covered. The description adds one genuinely useful behavioral detail, that results include freshness of the figures, but says nothing about snapshot timing, cache behavior or whether the numbers are point-in-time or rolling.

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?

Two sentences, front-loaded with the dataset-shape framing and closed with the usage rule. The colon-and-list construction is dense but every element carries information; no filler sentences.

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?

An output schema exists, so return values need not be described, and with no parameters the schema burden is nil. The description covers what is aggregated and the call-to-action context; the main gap is that it never scopes the dataset (which markets, which snapshot date) covered by the stats.

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 there is nothing for the description to disambiguate; the baseline of 4 applies. Nothing in the description contradicts or complicates the empty schema.

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?

It names a specific resource and enumerates what it reports: instrument counts split by pillar, category and risk, KYC-free count on the secondary market, and figure freshness. That is clearly distinguishable in kind from siblings like compare_assets or search_yield_products, though no sibling is named explicitly.

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?

"Use to ground a claim about coverage before making one" gives a concrete usage context, which is more than most stats tools offer. It stops short of naming a specific alternative (e.g. get_market_coverage) or stating when not to use it.

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