df_delete_dimension

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

Annotations already mark this as destructive and non-read-only, but the description adds meaningful context: deletion is permanent, and it is rejected under specific dependency conditions. This goes beyond what the annotations state and helps the agent anticipate failure cases.

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 front-loaded with a clear permanent-deletion warning. The first sentences are slightly redundant with the tool's name, but every sentence conveys useful operational information. No filler is present.

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 three-parameter delete operation with destructive annotations and no output schema, the description provides the essential context: what is deleted, that it is permanent, and the conditions that cause rejection. It does not mention alternative tools for resolving dependencies, but the core invocation conditions are sufficiently covered.

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?

project_id and version_id already have schema descriptions, and the description makes clear that dimension_id is the dimension being deleted. However, dimension_id itself remains undocumented in the schema and the description adds no further detail about formats, types, or how IDs relate. Schema coverage is adequate but not complete.

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 states the specific action ('Delete a dimension') and resource, and the resource name clearly separates it from sibling tools like df_delete_measure, df_delete_fact, and df_delete_dimension_group. The warning that deletion is permanent is unambiguous, so an agent knows exactly what this tool does.

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 usage: call this tool when a dimension should be deleted. It gives a rejection condition (belongs to a dimension group or referenced by a formula) but does not explicitly direct the agent to alternatives such as df_remove_group_dimension or df_delete_dimension_group. Usage context is present but not fully explicit.

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