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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds real behavioral context beyond that: the 0-100 scoring range, the high/medium/low band, the four derivation inputs, and the fact that it is the observatory auditing its own data — useful for interpreting the output. It does not describe return shape/pagination, but no output schema exists to constrain expectations further.

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 tight sentences: the metric and its meaning are front-loaded, with the derivation and usage tucked in after. Zero filler, and the scope ('per-country') plus scale arrive before any elaboration.

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 parameters and no output schema, the description carries the full burden, and it does explain the return value well (0-100 range, three-band label, four input dimensions) plus the intended interpretation, which is what an agent needs. Minor gaps remain — no mention of geographic coverage or staleness of the score itself — but nothing essential to invoking it correctly is 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 and schema coverage is 100%, so there is nothing for the description to clarify. The baseline for a no-parameter tool is 4, and nothing here requires a lower score.

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 clear resource (per-country data-trustworthiness scores) with scale and band, and explains what it measures (trust in Voidly's censorship measurements) and how it is derived (freshness, volume, stability, source diversity). It reads as a distinct meta-quality metric versus sibling tools like get_data_freshness or get_measurement_freshness, though it never names an alternative, so full sibling differentiation is only implied.

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?

Explicitly says when to use it: 'use it to weight conclusions about sparsely-measured countries,' which is a concrete usage condition. It does not name a specific alternative tool or state exclusions, so it stops short of the when/when-not/alternatives bar required for a 5.

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