get_pi_models

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

Annotations already indicate readOnlyHint=true and openWorldHint=true, so the agent knows this is a safe read operation. The description adds the qualifier 'available' and clarifies model types, but does not describe return format, pagination, or other behavioral details, which is fine given the 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?

The description is a single, front-loaded sentence with no filler words. It efficiently conveys the tool's scope and purpose.

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 listing tool with read-only annotations, the description fully covers what an agent needs: it names the resource type, specifies the domain, and implies the output is a list. No output schema exists, but the description gives enough context to 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 tool has zero parameters, so there is no parameter semantic burden. The description provides all necessary context about what will be returned, making the empty schema fully sufficient.

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 available Predictive Intelligence solutions, specifying the resource (PI models) and clarifying what these are (classification/similarity models). This distinguishes it from other list tools and ML training/evaluation tools in the sibling set.

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 style is a straightforward list command with clear context: use this tool to see what PI models are available. There are no explicit exclusions or alternative tool references, but the uniqueness of the purpose makes the usage implied enough.

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