Charts and tables on the statistics page

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered; the description adds real value by disclosing what is returned (dataset metadata, download addresses for CSV/JSON/SVG/screening-test/changelog, and row values with notes and reference keys). No contradiction with annotations. It does not mention result size or pagination behavior for large datasets, keeping it at 4.

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 default no-argument behavior and followed by the id variant. Every clause carries information: returned fields, downloadable artifacts, and per-dataset payload. No filler.

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?

There is no output schema, so the description carries full responsibility for describing return content, and it does so thoroughly — enumerating metadata fields, the five download address types, and the row-level payload. Combined with annotations that cover the safety profile and a fully documented single parameter, nothing needed to call 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?

Schema coverage is 100% and the enum of valid ids is already in the schema, so the baseline would be 3. The description goes beyond it by explaining the semantic consequence of the id parameter — that it returns that dataset's rows with values, notes and reference keys — and by giving concrete example ids.

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 specific verb and resource (lists chart and table datasets on the statistics page) and even enumerates the returned fields (id, title, topic, section, row count) plus the download artifacts. It also clarifies the second mode when an id is supplied, so the tool's scope is unambiguous against siblings like get_prevalence or get_reference. It stops short of naming a sibling it is not, so a 4 rather than a 5.

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 explicitly tells the agent how to switch modes: call with no argument to enumerate datasets, pass an id (e.g. chart-1, table-2) to get that dataset's rows. That is clear operational context. However, it names no alternatives or exclusions (e.g. when to prefer get_prevalence for a rate instead), so it lacks true when-not guidance.

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