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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false, and openWorldHint=false, so safety is covered. The description adds real behavioral context beyond that: results are aggregate and privacy-preserving, with no head counts, no individual activity, and small cohorts suppressed. It does not discuss refresh cadence or caching, but the privacy constraints are the material disclosure here.

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?

Front-loaded with the scoping statement, then the return fields, then the privacy constraints and the safety caveat. The middle sentence is dense with clauses but each clause earns its place; nothing is repeated from the schema or annotations.

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 and annotations carrying the safety profile, the description only needs to add meaning the structured data cannot. It does so by naming the fixed time window, the aggregation/privacy model, and the interpretive caveat, leaving nothing essential to calling the 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?

The tool takes zero parameters, so the baseline is 4 and there is no parameter semantics to add. The description correctly avoids inventing inputs (e.g., no date-range or cohort args), implicitly confirming the fixed last-7-days window.

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+resource (this week's most-researched peptides in the Peptide Library community) and pins down the exact return shape: rank, share of researchers, growth percentages, and what moved. This distinguishes it cleanly from siblings like search_research (query-driven) and get_peptide_categories (taxonomy), which an agent can differentiate without opening any schema.

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 framing as an aggregate popularity snapshot, and the closing caveat 'Popularity is not evidence of safety or effectiveness' usefully signals when not to lean on this output. However, it never names an alternative tool or a concrete precondition (e.g., 'use search_research for specific compounds'), so the when/when-not routing 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.