list_semantic_models

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

No annotations are provided, so the description carries the burden. It discloses the tool is a read-only listing operation and explains what the returned models contain, but it does not mention pagination, output size, or any side effects. For a simple list tool with no annotations, this is adequate but not rich.

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 short sentences, front-loaded with the core definition and followed by a concrete use case. No wasted words.

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 zero-parameter list tool with an output schema present, the description is nearly complete. It explains the purpose and contents of the result. It could mention whether the list is exhaustive or ordered, but that is minor given the output schema exists.

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 tool has zero parameters, so there is no schema burden. The description adds value by explaining what the returned objects represent (entities, dimensions, measures), which is the only semantic content an agent needs.

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 tool lists semantic models, defines them as the tables metrics are built on, and enumerates their contents (entities, groupable dimensions, aggregatable measures). It also gives a concrete use case, distinguishing it from siblings like list_metrics or list_dimensions.

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 provides a clear when-to-use signal: use it to understand why two metrics can or cannot be sliced the same way. It does not explicitly name alternatives or exclusions, but the use case is specific enough to guide an agent.

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