list_measures

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

With no annotations, the description must carry the behavioral burden. It does state that this is a listing operation and that the return value is a list of measures with definitions, which implies a read-only, non-destructive call. However, it does not discuss authentication, side effects, or behavior when no workspace/model is supplied.

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 the main purpose, followed by a clear Args/Returns structure. The 'ctx' line is arguably unnecessary noise since it is not an input schema parameter, but it does not meaningfully hurt readability.

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 list operation, the description covers the essential behavior and return value. The lack of an output schema and annotations raises the burden, and the description does not clarify how optional workspace/model relate to the current context or how this tool fits with measurement-related siblings.

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 coverage is 0%, so the description must add parameter meaning. It does add 'Name or ID' and 'optional' for workspace and model, which goes beyond the schema's bare property names and null defaults. It stops short of explaining how the optional parameters interact with a default/current context, so it 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 clearly states the action ('List') and the resource ('all DAX measures in a semantic model'), so an agent knows exactly what the tool does. It does not explicitly differentiate itself from siblings like get_measure or list_semantic_models, which keeps it at 4 rather than 5.

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?

No guidance is given for when to use this tool instead of get_measure, list_semantic_models, or get_model_schema. The optional workspace/model arguments are described, but there is no explanation of when they are needed or what happens if they are omitted.

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