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

Annotations declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false, so safety is covered. The description adds valuable context: that results are computational predictions, not validated clinical findings, which is critical for an agent to appropriately convey uncertainty. This goes beyond annotations by providing epistemic status.

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?

The description is a single, compact paragraph that front-loads what the tool returns and immediately disclosures the evidence tier. Every sentence adds value: first sentence lists outputs, second sentence warns about the computational nature. No fluff.

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?

Given the tool's simplicity (single parameter, no output schema, low complexity), the description covers all necessary aspects: what is returned, the nature of the data, and the safety profile via annotations. No critical information is missing for an agent to use it 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 schema already fully documents the single parameter (experiment_id) with an example and format, so schema coverage is 100%. The description does not add further parameter-specific details, but with full coverage a baseline of 3 is appropriate. No additional nuances are needed.

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 states a specific purpose: retrieving differential-expression results for one HERB transcriptomic experiment, listing the exact outputs (top up/down-regulated genes, GO terms, KEGG pathways, connectivity-map counts). This clearly distinguishes it from sibling tools like 'herb_experiments' (likely listing experiments) and 'herb_detail' (general entity detail).

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 description implies it is used when an agent needs results for a single experiment, given an experiment_id. It does not explicitly state when not to use it or name alternatives, but the context of sibling tool names and the schema (requiring an experiment_id) makes usage clear. No explicit exclusions are provided, but it's adequate for most call scenarios.

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