Update DataLens dataset

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

Annotations already declare readOnly=false, idempotent=true, destructive=false, so the safety bar is partly met. The description goes further, disclosing optimistic-concurrency behavior (older revisions are refused), the need to re-read after saving, and what the reply contains, which is meaningful context beyond the annotations.

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?

Three short, front-loaded sentences with no filler; the core action leads and the workflow caveats follow. The parenthetical '(measured)' and heavy backtick usage are slightly cryptic but do not bloat the text.

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 carries the return-value burden and partially does so ('the reply holds the content and the revisions; its id is null'). It also covers revision conflicts and the validate workflow, though the ``publish`` vs ``save`` mode of the nested ``data`` object is left entirely to the referenced schema.

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% and the ``data`` parameter already points to ``schema_get`` for the ``DatasetUpdate`` type, so the schema does the heavy lifting. The description only reinforces that the ``dataset`` key replaces content, adding marginal meaning over the schema baseline of 3.

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 states a specific verb and resource — saving a dataset by reading it, changing it, and returning it whole — and explicitly declares that ``data.dataset`` replaces the content. It differentiates from siblings by naming ``datasets_validate`` as the pre-check tool, so an agent can tell it apart from create/validate/delete without inspecting schemas.

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 concrete workflow context: validate a change first with ``datasets_validate``, and re-read the dataset after every save because older-revision content is refused. This is rich, actionable guidance, though it stops short of naming when-not-to-use cases or contrasting with ``datalens_datasets_create`` for net-new datasets.

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