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

Beyond the readOnly/idempotent annotations, it discloses substantive semantics: missing means no figure, never a zero; unmatched source keys are reported but never mapped onto a current place; drafts are listed as not served without counts. These are exactly the behaviors an agent must know to interpret results correctly.

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?

It is front-loaded with the core behavior and every sentence carries semantics (mode selection, safety-of-interpretation rules, edge-case handling). It is dense and runs long, but no sentence is padding, so it stops short of a 5 only for sheer length.

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 an output schema present, the description need not document return structure, and it covers the remaining gaps: both operating modes, pagination context via the schema, and interpretation caveats. An agent has everything needed to call and read this tool correctly.

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?

Schema coverage is 100%, so limit/offset are already documented; the baseline is 3. The description still adds real meaning by explaining what passing vs. omitting `dataset` yields (summary of every dataset vs. that dataset's missing places by region), which the schema only paraphrases.

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 concrete verb+resource: it reports, per dataset, how many places it covers out of how many at its grain, plus the source's coverage caveat. It also clearly distinguishes the two modes (summary vs. gap listing) from siblings like list_datasets, from which the slug is drawn.

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 an explicit trigger ('Use it before telling anyone data does not exist for a place, and to explain a null'), names list_datasets as where the `dataset` slug comes from, and specifies which mode applies when the parameter is omitted versus passed. Nothing about when to reach for this tool is left to inference.

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