arf_exclude

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

Adds substantial behavior beyond the readOnlyHint annotation: state is held in the session, filtered_features itself is untouched so mode=remove/clear restores it, and unmatched specifications go to unmatched_samples rather than aborting. This resolves what would otherwise look like a readOnly-versus-mutation tension by clarifying only session state changes.

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?

Front-loads purpose and the non-destructive guarantee, then a prerequisite line, then a clean per-argument breakdown. Slightly long, but nearly every clause adds actionable detail; only marginal trimming would be possible.

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?

No output schema exists, so the description proactively names the relevant return fields (resolved_samples, unmatched_samples) and the four modes, giving an agent everything needed to call and interpret the result 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?

Schema description coverage is 0%, so the description carries the full burden, and it does: exclude_samples token syntax (`"ILG_6h"` resolves across Class ID and sample names), exclude_spots as MasterAlignmentID ints, and all four mode values with their meanings. This is far beyond what the bare schema conveys.

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?

States a specific verb pair (manual exclusion/re-inclusion) against a specific resource (PCA outlier samples or named peaks), and qualifies the operation as reversible and non-destructive up front. An agent understands immediately this is a curated edit to analysis exclusions, not a data mutation.

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?

Explicitly states the prerequisite (run arf_parser first) and where the exclusion propagates (re-runs of arf_parser / arf_preprocess → arf_pca_preprocessed / arf_differential). It does not name an alternative tool for the same job, so it stops short of full when-to-use-vs-alternative guidance.

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