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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds the conceptual behavior (testing whether a ranking is concordant across conditions) but says nothing about the design prerequisite of balanced measurement, cost of large permutation counts, or interpretation of the result. A 3 is appropriate given annotations carry the operational safety.

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?

A single front-loaded sentence that frames the question before the qualifier, with no redundant or filler text. The only cost is that 'Ask whether' is a slightly soft verb for what is a permutation-based statistical test.

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 a full input schema, an output schema, and safety annotations, the description only needs to add framing — which it does. It is nonetheless incomplete on the one thing not covered elsewhere: how this differs from sibling ranking tools and what prerequisite data shape (every gene measured in every sample) implies for the study design.

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 description coverage is 100%, including detailed notes on cq_table format, permutation bounds and per-permutation seeding. The description contributes nothing about any parameter, so the baseline 3 applies — the schema is doing all the semantic work 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?

The description names a specific analytical question — whether a reference-gene stability ranking holds across conditions — and adds the discriminating detail 'rank well everywhere rather than in one tissue or treatment.' That is clearly distinct from a within-dataset ranking, but it never names the close siblings (rank_reference_genes, discover_reference_stability), so differentiation is inferred rather than stated.

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 phrasing 'Ask whether ... holds across conditions' implies the situation (multi-condition Cq data where a reference must be stable everywhere), but there is no explicit when-to-use, when-not-to-use, or pointer to the single-condition alternative. An agent must infer the routing decision from context alone.

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