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

Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds genuine behavioral value beyond that: it explains the row shape (ids, status, headline), the lineage taxonomy, and notably what is excluded ('never the candidate's output or proposed target'), which prevents an agent from expecting payload data. That exclusion is exactly the kind of context annotations cannot carry.

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?

Two dense sentences with no filler, and the row-content framing is front-loaded. The content is somewhat jargon-heavy ('lineage', 'propagated', 'superseded'), which slightly taxes parsing, but no sentence is wasted.

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, the description must carry return-value expectations, and it does: it specifies the row columns and explicitly rules out output/proposed-target payloads. Paging behavior is left to the schema, which documents limit/offset adequately. For a 5-param read-only list tool this is close to complete.

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%, with limit, offset, datasetId, customerId, and the admissionStatus enum each documented in the schema itself. The description adds no parameter-level syntax or filtering behavior beyond what is already structured, so the baseline 3 applies.

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 details what fields appear per candidate row (admission state, lineage, first failing check) but never cleanly states the act of listing dataset examples; the term 'candidate' is used without grounding it to the dataset concept. It does not differentiate itself from siblings like list_datasets or list_dataset_versions. An agent can infer it returns example rows, but the framing is oblique.

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?

There is no explicit when-to-use statement and no mention of alternative tools such as list_datasets (to obtain the datasetId) or list_dataset_versions (to obtain a specific version). The admissionStatus filter's purpose is left to the reader to infer. Context is entirely absent.

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