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

Annotations already declare readOnlyHint=true, openWorldHint=false, and destructiveHint=false, so the safety profile is covered. The description adds genuinely new context: that computation runs on a real algorithm engine rather than being LLM-generated, and it enumerates the outputs (differential genes, enrichment results) despite there being no output schema. It does not mention runtime or data-download behavior for large GEO series.

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 structure is well front-loaded: bracketed scope, then when-to-use, then returns, then the engine note. Some redundancy exists because the bracketed label duplicates the annotation title, but every element adds information and nothing is padding.

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 multi-stage pipeline tool with four parameters and no output schema, the description covers purpose, trigger condition, and return content, which is the essential core. It stops short of the operational detail such a heavy pipeline needs: expectations about input validity, runtime, and how partial failures are handled.

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 geo_id, species, pval_cutoff, and logfc_cutoff are already documented in the schema. The description adds no parameter-level meaning (no defaults, no example cutoffs, no species behavior), so the 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 names a specific resource (GEO dataset) and a specific composite operation: differential analysis, enrichment, and immune infiltration in one pipeline, plus the returns (differential genes, enrichment results). It implicitly distinguishes itself from single-step siblings like act_deg_analyze and act_enrich by being a full pipeline, but never names those siblings explicitly.

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?

It gives a when-to-use condition: user supplies a GEO ID and wants the full differential/enrichment/immune-infiltration workflow. It does not say when to prefer the single-step siblings (act_deg_analyze, act_enrich, act_tme_analyze) or what prerequisites the GEO dataset must satisfy, so routing among similar tools 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.