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

Annotations already declare readOnlyHint and openWorldHint=false, so safety is covered; the description adds genuine behavioral context by disclosing that the tool reports tagged/untagged counts plus per-category domain counts and that it is an honest self-disclosure of a categorization blind spot. No pagination, timeframe, or freshness caveats are given, which is the remaining gap.

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, front-loaded with the core question it answers and the return contents. The em-dash appositive about the 'blind spot' is slightly ornamental but still earns its place as framing for a coverage metric.

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 must carry the return-value burden, and it does name the outputs (tagged/untagged counts, per-category counts). It omits things like when the snapshot was taken or whether coverage is global vs. per-region, which an agent might reasonably want.

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 schema cannot add semantic meaning and the baseline is 4. The description correctly implies a no-argument call by not referencing any filters.

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 measurement (what share of Voidly's measured-domain corpus carries a Citizen Lab content category) rather than a vague verb, and signals what it is not by tying itself to 'by category' analyses. It stops short of naming the sibling tools it most resembles (get_categories, get_censorship_by_category) to make the distinction explicit.

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?

It gives a clear when-to-use context: gauge how complete any 'by category' analysis is. There is no when-not guidance and no named alternative, so the routing against sibling category tools is left partially to inference.

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