dataset_set_sample_metadata

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

Goes well beyond the annotations by disclosing all-or-nothing validation ('no partial application'), that errors leave session.dataset untouched and can be safely resent, and the precise invalidation cascade (preprocessing-input changes kill matrices/PCA/differential; group-only changes kill only differential and preserve PCA). This is exactly the mutation-safety context an agent needs.

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 core purpose is front-loaded and each substantive sentence (validation atomicity, invalidation rules, changed_fields) adds new information. The closing paragraph on resolve_comparison/run_comparison is somewhat tangential but still connects to the sibling relationship.

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 single-parameter mutation tool, the description covers validation behavior, failure semantics, invalidation consequences, and idempotency-relevant resend safety. The only gap is relying on an external spec for column meanings, which is acceptable given the tool's scope.

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?

Only one parameter (manifest_path) with 0% schema coverage, so the description must carry the meaning. It does so by naming the manifest format (sample-manifest.v1) and the columns it expects (role/batch/injection_order/qc_pool/include/sample_id/source_file), though it defers exact type/format to an external spec doc.

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 chain (read → validate → bulk-apply) on a specific resource (sample-manifest.v1 into DatasetState), and explicitly positions itself against the dataset_differential path. An agent can tell exactly what it does and what it is not.

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?

Explains the primary use case (correcting group/batch/injection_order/qc_pool mistakes without re-running the upstream Console) and contrasts it with calling dataset_differential directly. It gives clear context but does not enumerate explicit 'do not use when' exclusions.

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