list_inference_models

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

No annotations are provided (no readOnlyHint). The description accurately states it lists models, which implies read-only behavior. However, it lacks details on performance, pagination, or any potential side effects, which is acceptable for a simple 0-param tool but could be more transparent.

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 concise sentences that front-load the purpose and include key details about what is listed. No redundant phrases.

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 0-param tool with no output schema, the description is sufficient. It explains the scope (platform + trained models) and the API context. Could mention that output is a list of model IDs or similar, but not necessary.

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?

Input schema has 0 parameters, so description does not need to elaborate on params. Baseline 4 is appropriate as the description adds no param info but schema coverage is 100% and no params exist.

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 models for inference via Tuning Engines API, distinguishing it from generic model listing tools like `list_models` by specifying the inference context and included model types (platform + deployed trained).

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 implies use when needing inference-available models, but does not explicitly state when not to use or provide comparisons to siblings like `list_models` or `list_catalog_models`. The context is clear enough for a simple tool.

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