Get DataLens dataset

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

Annotations already cover readOnly/idempotent/openWorld, so the description's job is lighter. It adds a non-obvious behavioral detail: unknown source or field kinds are returned as-is rather than normalized or rejected, which an agent cannot derive from 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?

Two short sentences, front-loaded with the summary of what is returned before the cross-reference detail. The phrasing is slightly compressed but nothing 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 carries the burden of explaining returns and does so by naming sources/joins/fields and the result_schema guid listing. Remaining minor gaps (pagination, error behavior) are not critical for a single-record get.

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%, so rev_id and workbook_id are already documented in the schema. The description adds no extra meaning about revision selection or workbook scoping, so the baseline of 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?

States a specific verb and resource ('One dataset by id') and enumerates exactly what the result contains (sources, joins, fields). This distinguishes it from datalens_datasets_data_get and other dataset siblings without needing 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?

It references datasets_data_get and the relationship between result_schema guids and its columns, which implies the division of labor (metadata here, data there). However, there is no explicit when-to-use/when-not guidance or prerequisite statement, leaving the routing to inference.

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