update_template_measure

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

With no annotations, the description carries the full burden and excels: it discloses the write behavior (edits JSON on disk, written back in place), the safety .bak guard, the failure condition if the measure does not exist, and the no-Desktop operation. This goes well beyond the schema and annotations.

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?

Three sentences, all dense with information: the first states action and mechanism, the second explains omission semantics and failure, the third notes backup safety. No filler, no redundancy, and the verb 'Update' is front-loaded.

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 mutation tool with no annotations and no output schema, the description is remarkably complete. It covers what is updated, where (closed .pbit), how (JSON on disk, no Desktop), the behavior for omitted fields, failure conditions, and backup safeguard. An agent can confidently invoke it with just this description and the schema.

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 covers all six parameters with descriptions (100% coverage), so the baseline is 3. The description adds critical semantics: 'Any omitted field is left unchanged' clarifies the meaning of null/omit for the optional fields, and 'Fails if the measure does not exist' ties to the required name/table. This extra context justifies a 4.

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 ('Update an existing measure's DAX expression, format string and/or display folder'), the target resource ('a closed .pbit template's model'), and the method ('editing the DataModelSchema JSON on disk with no Power BI Desktop'). This distinguishes it from sibling update tools like update_measure or set_measure_properties that likely operate on open models.

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?

It provides explicit context: use for closed .pbit templates without Power BI Desktop, and clarifies that omitted fields are preserved. However, it does not name alternatives or state when not to use it, so it falls short of full guidance.

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