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. Beyond that, the description supplies real behavioural context: each table is one falsification test, tables are applied in the given order, and a gene-level score can contradict the hypothesis as predicted by the table's direction. It stops short of saying what a failed run returns or how partial evidence is reported, but the falsification model is genuinely informative.

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 long run-on sentence loaded with nested parentheses and an inline example, which makes it harder to parse than necessary. The opening clause is well front-loaded with the core action, but the rest could have been split into discrete sentences about inputs and decision rule.

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 six-parameter tool with nested objects and an output schema, the description establishes the conceptual model but leaves gaps: no routing versus similar discovery/validation tools, no statement about table ordering failure semantics, and no mention of what the returned evidence looks like conceptually. Adequate but not complete.

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%, so every parameter including gene, alpha, kappa, tables and the record plumbing is already documented in the schema. The description adds conceptual framing for `tables` (one table = one test, ordered) but no syntax, formatting, or interpretation detail beyond what is present in structured fields. Baseline 3 is appropriate.

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 gives a specific verb and object: test a hypothesis that a gene drives a phenotype, operationalised as POPPER-style falsification over evidence tables. That is concrete enough that an agent knows what the tool does. It does not, however, distinguish itself from near neighbours such as validate_gene_trait, run_discovery_programme, or rank_next_tests, which pull in similar territory.

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 rather than stated: you would call it when you have per-gene score tables and a directional prediction to test. There is no explicit when-to-use, no when-not-to-use, and no naming of an alternative tool for the related cases (e.g. validate_gene_trait). The agent has to infer routing from the mechanics described.

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