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 by structured data rather than the description. The description adds the negative-binomial method and states the return is a DEG list, but says nothing about default thresholds, multiple-testing correction conventions, or failure modes on malformed matrices — a modest increment over the annotations.

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 pipe-delimited template is front-loaded with purpose and the USE WHEN clause, so the most important information comes first. The bilingual ALIASES block is largely keyword stuffing that consumes a meaningful share of a short description without adding decision-relevant content.

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 4-parameter read-only analysis tool with a simple contract, the description covers what it computes and what it returns at a high level, and no output schema is needed given the 'RETURNS: 差异基因清单' line. What is missing is the structural contract for the nested 'data_json' matrix (gene rows vs sample columns, group vector alignment) and any statement of default FDR/log2FC values.

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 the four parameters are technically documented, and the description correctly implies a count matrix plus thresholds. However it adds no meaning beyond the schema — notably it never explains how 'groups' should align with matrix columns or the orientation of the nested 'data_json' object, which is the main ambiguity risk.

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 verb and resource ('RNA-seq differential analysis') and scopes it with 'differential genes from RNA-seq count matrix (negative binomial)'. That is clear enough to act on, but it never distinguishes itself from the sibling tool 'differential_expression' (or 'atac_diff'), so an agent comparing the two must open both schemas to choose.

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?

'USE WHEN: differential genes from RNA-seq count matrix (negative binomial)' gives an implied input condition and hints at the statistical model, which is genuine guidance. However there are no exclusions, no prerequisites (e.g., raw counts vs normalized, minimum replicates), and no pointer to sibling alternatives, so routing is only partially supported.

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