add_measure_with_validation

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

No annotations are provided, so the description carries the behavioral burden. It transparently discloses the lint gate blocking writes, the fail_on_severity default, and dry_run behavior. However, it doesn't explain what happens on success (return value), whether existing measures are overwritten, or any required permissions, leaving gaps.

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?

Two sentences, front-loaded with the core action and immediate context about the lint gate. The dry_run explanation is useful and directly attached to the relevant parameter. Could be slightly more structured (e.g., listing key params) but is appropriately brief.

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?

With 11 parameters sin which schema coverage is 0%, the description leaves most parameters unexplained, including target, measure_name, expression, is_hidden, runtime_check, and measure_writer. It also references fail_on_severity in the description but that parameter does not appear in the input schema, which is confusing. No output schema existsandar the description doesn't state what successful execution returns, so an agent lacks key info to confidently call this tool.

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 0% with 10 parameters, so the description must compensate. It only clarifies fail_on_severity and dry_run. Worse, fail_on_severity is mentioned but not present in the input schema while other params like runtime_check, measure_writer, and format_string remain unexplained.

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 identifies the tool's job: 'Add a DAX measure with mandatory lint validation.' It uses a specific verb and resource, and the validation detail sets it apart from a plain add-measure tool. However, it doesn't explicitly name a sibling it differs from, so it falls just short of full differentiation.

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 explains the lint gate behavior and the dry_run option, which implies the intended workflow: use this to add measures with validation, or dry-run to preview findings. It does not explicitly state when not to use this tool or name a better alternative among siblings, leaving the selection reasoning partially implicit.

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