update_measure_dax

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

With no annotations provided, the description fully owns the behavioral disclosure. It highlights the model-modifying nature, the double opt-in (confirm flag and write access), and the validation behavior ('an invalid expression is rejected rather than saved'), providing essential context beyond any schema.

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 compact, front-loaded with the core action and a prominent warning, and every sentence contributes value. It covers purpose, permissions, workflow, and validation in five sentences without redundancy.

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 simple mutation tool and the presence of an output schema, the description provides all essential context: what it does, when to use it, prerequisites, and safety behavior. The inclusion of permission requirements and validation makes it complete enough for an agent to select and invoke 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?

The schema has zero property descriptions (0% coverage), so the description must compensate. It explicitly explains the critical confirm parameter ('must pass confirm=True') and implies measure/new_expression through the main sentence, but it does not mention model_label or provide format/syntax details. Partial coverage, not comprehensive.

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 a specific verb and resource: 'Write a new DAX expression for a measure.' It also clearly flags 'MODIFIES THE MODEL,' which distinguishes this from the read-only siblings like show_measure. This is unambiguous and differentiates the tool's intent.

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 explicitly instructs to use show_measure first: 'Always show the user the current expression with show_measure and get their agreement before calling this.' It also details the exact conditions required (confirm=True and write access), making it clear when and how to use this tool versus alternatives.

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