df_remove_group_dimension

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

Annotations declare destructiveHint=true, and the description reinforces this with a prominent warning: 'PERMANENTLY DELETES DATA IN DATAFORGE.' It adds behavioral context by explaining that only the group membership is removed, not the dimension itself, which is valuable beyond the annotations. It does not contradict annotations and provides additional clarity on scope.

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 extremely concise, consisting of two sentences with zero filler. The critical warning about permanent deletion is front-loaded, and the clarifying note about the dimension remaining in the RMD follows immediately. Every word 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?

Given the tool's simplicity (a single removal operation with no output schema) and the annotations covering idempotency and destructiveness, the description covers the essential points: what it does, what is deleted permanently, and what is preserved. It does not discuss edge cases like removing a non-existent dimension or behavior on repeated calls, but the idempotentHint annotation covers repeat behavior. Overall, it is sufficiently complete for an agent to call it correctly.

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 50% (project_id and version_id have descriptions; dimension_group_id and dimension_id do not). The description says 'Remove one dimension from a group,' which implicitly maps dimension_id to the dimension and dimension_group_id to the group, but it does not explicitly explain these parameters. The names are somewhat self-explanatory, but the description adds minimal value beyond what the schema already conveys, and it does not fully compensate for the missing schema descriptions.

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 clearly states the action: 'Remove one dimension from a group' with a specific resource (dimension group). It also clarifies that the dimension itself remains in the RMD, distinguishing it from deleting the dimension entirely. This differentiates it from siblings like df_delete_dimension_group or df_set_group_dimensions, making the purpose unambiguous.

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?

The description implies when to use this tool (to remove a single dimension from a group) and notes the dimension is not deleted, but it does not explicitly mention alternatives like df_set_group_dimensions or df_delete_dimension, nor does it state conditions for when to prefer this over them. The guidance is implicit rather than explicit, which is adequate but not thorough.

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