df_get_data_mart

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context by stating exactly what is included in the returned data mart definition, which is valuable because there is no output schema.

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?

The description is a single front-loaded sentence that immediately states the operation and scope, then lists the included components. There is no filler or redundancy.

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?

For a moderately complex retrieval tool with no output schema, the description provides a useful high-level inventory of the response contents. However, it omits guidance on the optional language and cache parameters, does not differentiate from get_data_mart_view, and lacks enough detail for an agent to fully predict the returned structure.

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 only 40%, and the description adds no parameter-level meaning. It does not clarify data_mart_id, language, or use_cache, and 'one data mart' only weakly maps to data_mart_id. The schema's existing descriptions for project_id and version_id carry most of the weight.

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 uses a specific verb and resource ('Get one data mart in full') and enumerates the contents: source fact tables, selected measures, facts and dimensions with aggregation and filter settings. It is clear and self-contained, but it does not explicitly contrast with sibling tools like df_get_data_mart_view or df_list_data_marts.

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?

No guidance is given about when to use this tool versus alternatives such as df_list_data_marts or df_get_data_mart_view. The agent must infer from the name and the word 'full' that this is the detailed single-data-mart getter, which is not reliable.

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