dataset_differential

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

Despite annotations only providing readOnlyHint=true, the description discloses critical non-obvious behavior: full results and volcano points are NOT returned but stored in session.dataset.last_differential, with export handled by dataset_export_differential. It also notes that samples missing from the preprocessed matrix or flagged as QC/blank are excluded and named in a caveat, which is essential behavioral context for interpreting results.

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 front-loaded with the core action, then follows with parameter meanings and a bolded directionality note. It is appropriately detailed for a 7-parameter tool but slightly dense; every sentence earns its place, though the structure could be marginally tighter by grouping the label parameters.

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 no output schema, 0% schema coverage, and 7 parameters, the description does a strong job covering the essential execution context, parameter semantics, session storage behavior, and export routing. The only gap is the undocumented group_a_label/group_b_label parameters, which are minor but leave a small incompleteness for a tool of this complexity.

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 0%, so the description carries the full burden. It explains the defaults and semantics of q_threshold (BH-FDR significance level, default 0.05), log2fc_threshold (absolute value threshold for significance, default 1.0), and log_transform (log2(x+1) space, with rationale that MS intensities are near log-normal). It also critically clarifies the directionality: positive log2FC means group_b is higher, with group_a as baseline. However, group_a_label and group_b_label (2 of 7 parameters) are not mentioned at all.

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 opens with a specific verb and resource: 'Execute differential analysis (Welch t-test + BH-FDR) on 2 groups using a preprocessed DatasetState'. This clearly distinguishes the tool from siblings like dataset_export_differential (export) and dataset_status (inspection). An agent can immediately understand the purpose without opening the schema.

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 states a clear prerequisite (preprocessed DatasetState) and explicitly tells the agent how to find valid sample names ('check usable names and roles in dataset_status's `samples` TSV'). It also names the alternative for exporting results (dataset_export_differential). However, it does not explicitly state when NOT to use this tool versus siblings like arf_differential or dataset_statistic.

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