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

Annotations already declare readOnlyHint/idempotentHint/destructiveHint=false, so safety is covered. The description adds value beyond that: values are NHIS-published, updated only once per year (freshness), and it prescribes behavior for gaps (don't fabricate, redirect to url/contact). That is genuine behavioral context.

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?

Content is front-loaded with the returned fields, followed by freshness and the no-guessing rule. The bilingual duplication roughly doubles the length, but each sentence carries distinct information with 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?

With no output schema, the description appropriately compensates by enumerating the returned fields, plus freshness and missing-value handling. Complete enough for an agent to call a zero-parameter stats tool correctly, though a hint about the value scale or format would round it out.

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 baseline is 4. The description lists the data categories surfaced, which is more useful than anything a parameter schema could add here.

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?

It clearly states the tool returns site-wide aggregate statistics and enumerates the specific returned items (data baseline date, national institution count, grade distribution, counts by benefit type, source/disclaimer). This distinguishes it from institution-level siblings like get_institution and search_institutions, though it does not name an alternative 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?

Usage is implied by the aggregate nature of the resource, and it gives a handling rule ('never guess missing values; point to the url or contact page'), but it never states when to prefer this over siblings like compare_institutions or list_regions. No explicit when/when-not routing.

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