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

No annotations are supplied, so the description carries the full disclosure burden. It does add real behavioral content: passing api_key scopes the response to 'your team's attempts', and the safety constraint against real patient data is spelled out. However, it never states that the operation is read-only, never describes response structure or size, and the parenthetical about 'what a name checker used as written scores' is too cryptic to count as disclosure.

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 a single compact sentence, which is appropriately sized, but the phrasing is dense and metaphor-laden ('cleanup season', 'the bar to beat', 'written the messy way real ones are'). The key operational facts — what is returned and that api_key personalizes results — are buried mid-sentence rather than front-loaded.

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?

For a zero-required-parameter read tool with no output schema, the description does cover the main return payloads (synthetic reports, answer format, scoring rules, benchmark scores, team attempts). What is missing is any statement of shape or volume of the response and any pointer to which sibling to call next, which matters given the crowded HLA sibling set.

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% for the single api_key parameter, so baseline is 3. The description earns an extra point by explaining the semantic effect of supplying it — the result becomes team-scoped ('With api_key, your team's attempts') rather than generic, which the schema ('Your API key from register_agent') does not convey. It also implies the parameter is optional by phrasing it conditionally.

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 names the resource ('synthetic HLA typing reports') and what comes with it (answer format, scoring rules, the bar to beat, team attempts), but it never states a verb — nothing says whether this retrieves, lists, or describes. The framing 'HLA lab-report cleanup season' is thematic rather than operational, so the agent must infer that this is a briefing/lookup tool. It is distinguishable from hla_get_report and hla_submit, but only implicitly.

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?

There is no explicit when-to-use guidance and no exclusion against siblings such as hla_get_report or hla_vet. The mention of 'the answer format, the scoring rules and the bar to beat' hints this is a pre-submission briefing, but that sequencing is left entirely to inference. Only the safety note ('Never send real patient data') is explicit.

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