Datasets List Data Model Sources

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=true, so safety is covered. The description adds genuinely non-obvious behavior: it auto-follows Sigma pagination to the last page and returns entries from every page with nextPageToken always null. That is real value beyond the structured fields, though it omits any warning about large/slow result sets for big models.

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 sentences, front-loaded with the purpose, then a compact statement of the pagination contract. Nothing is redundant with the schema or annotations, and every clause carries information.

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?

An output schema exists, so the description need not explain return values, yet it usefully explains that page keys are flattened. Combined with read-only annotations and a single required param, the definition is nearly sufficient; only the provenance/format of data_model_id is missing.

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?

There is one parameter (data_model_id) with 0% schema description coverage, so the description would need to carry it. It implies the ID identifies the data model whose sources are listed, but gives no format, source of the ID, or error behavior for an invalid ID.

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 (List) and resource (sources a data model reads from), and the parenthetical enumerates exactly what counts as a source (tables, datasets, other data models), which separates it from siblings like datasets_list_data_model_elements, _columns and _lineage. It never names a sibling explicitly, so differentiation is inferred rather than stated.

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?

Usage is only implied: an agent can infer this is the tool for 'what feeds this data model'. There is no explicit when-to-use, no prerequisite (e.g. that data_model_id must come from datasets_list_data_models), and no statement about when to prefer data model lineage instead.

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