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

Annotations already cover the safety profile (readOnlyHint=true, destructiveHint=false), so the bar is lower. The description adds genuinely useful behavioral context beyond annotations: the hourly recomputation cadence, a staleness warning (quote computed_at, not request time), and the interpretation caveat that these are counts rather than a success rate. This is real value-add with no contradiction to the readOnly/destructive hints.

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?

A single dense paragraph that front-loads the core purpose and packs provenance, cadence, metric list, staleness warning, and interpretation caveats without redundancy. It is efficient but slightly run-on; separating the staleness warning into its own sentence would improve scannability marginally.

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 carries the burden of explaining returns, and it largely does: it enumerates the metrics, clarifies that these are counts not rates, and warns about data freshness. It could specify the response format or the computed_at value's format, but the substance an agent needs to interpret results is present.

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?

There are zero parameters, so the empty schema (trivially 100% covered) leaves nothing to document. The description instead explains the data semantics of what the tool returns — the metric set and the market-cap-at-logging entry definition — which is the relevant semantic content for a 0-param tool. Baseline 4 is warranted.

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 states a specific resource (Robinhood Chain statistics) and provenance (PumpPill's index), enumerating concrete metrics: tokens seen, forward-measured outcomes, doubled/5x/10x/$100K crossings, stock-quoted counts. This identifies it as the aggregate stats tool among siblings like rh_scan_now or rh_flagged_recent, though it never names a differentiating sibling 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?

The description offers interpretive guidance — 'Counts, not a success rate', 'entry is the market cap at logging', and 'Quote computed_at, not the time you asked' — which helps an agent use the results correctly. However, it never states when to prefer this tool over overlapping siblings such as rh_scan_now or outcomes_measured, so no explicit when/when-not routing exists.

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