Datasets Get Data Model Spec

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=true, so the safety profile is covered. The description adds that the result is a full JSON code representation containing tables, columns, metrics and relationships, which is somewhat useful, but it omits auth requirements, sizing concerns, or any note about the open-world access implied by the annotation.

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?

A single front-loaded sentence with no filler: verb, resource, format, and payload contents in order of importance. Every clause earns its place.

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?

Because an output schema exists, the description need not explain return values, and the annotations cover safety. What remains missing is disambiguation from the sibling read tools and any information about the required id, which matters for a tool among many similarly named dataset getters.

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 exactly one parameter, data_model_id, and schema description coverage is 0%, so the schema only supplies the name and string type. The description does not define what a data model id is, where to obtain it, or its format, leaving the single parameter's semantics under-specified.

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 gives a specific verb ('Get') and resource ('full code representation (JSON spec) of a data model') and enumerates the contents (tables, columns, metrics, relationships). This partially distinguishes it from sibling datasets_get_data_model and datasets_list_data_model_elements, though it never explicitly contrasts the 'spec' retrieval with those alternatives.

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 statement of when to use this tool versus the numerous sibling getters (datasets_get_data_model, datasets_list_data_model_elements, datasets_list_data_model_columns). Usage is only implied by the returned payload, with no conditions, prerequisites, or exclusions.

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