df_delete_measure

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

Annotations already mark the operation as destructive. The description adds valuable behavior beyond that: the deletion is permanent and will be rejected with a conflict if another element's formula references the measure. This is useful disclosure.

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 short and the key warning is front-loaded. The all-caps sentence is somewhat redundant with destructiveHint, but the conflict-rejection detail 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?

For a simple delete operation, the description covers what is deleted, the destructive nature, and an important failure mode. It does not explain return values or prerequisites, but with no output schema these are less critical.

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?

The schema describes project_id and version_id, covering 67% of parameters. The description adds no parameter-specific detail, and measure_id remains undocumented, but its meaning is evident from the tool name and the 'Delete a measure' sentence.

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 names the specific resource ('measure') with a clear action ('Delete') and adds the critical constraint 'PERMANENTLY DELETES DATA.' This differentiates df_delete_measure from sibling delete tools like df_delete_dimension and df_delete_fact.

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 implied by the action and resource: use this tool to delete a measure. However, there is no explicit when-to-use guidance or contrast with sibling delete tools, so an agent must infer context from the tool name.

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