list_inference_services

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

No annotations are provided, so the description carries the behavioral burden. It conveys that this is a non-mutating list operation and names the returned attributes, which is basic transparency. However, it does not disclose scoping behavior, error cases, or any side effects beyond what 'List' implies.

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?

One sentence, front-loaded with the action and resource, followed by a compact field list. There is no filler or redundant restating of the tool name.

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?

An output schema exists, so return structure is partially covered, but the optional parameters are undocumented and no sibling differentiation is provided. An agent cannot tell how to scope the query or why this tool should be chosen over list_model_servers.

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 description coverage is 0% and the description never mentions cluster or namespace. Defaults of empty strings exist, but the meaning of those defaults is left entirely unexplained. The description does nothing to compensate for the absence of schema-level parameter documentation.

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 names a specific action and resource, 'List InferenceServices', and enumerates the meaningful output fields (readiness, model format, storage URI, URL, age). It is clear but does not contrast itself with sibling tools such as list_model_servers, so it stops short of full differentiation.

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?

There is no guidance on when to use this tool instead of alternatives, no mention of how it differs from related inference-service or model-server tools, and no explanation of when the cluster or namespace parameters should be set. The verb 'List' implies the general use case, but nothing more.

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