inference_list_serverless_inference

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

Without annotations, the description carries the burden of behavioral disclosure. It clearly states that the operation returns a list of all subscriptions and enumerates the output fields, which is useful. However, it does not mention that the operation is read-only, any potential side effects, or pagination behavior, which could be relevant for a list-all endpoint.

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 primary actionabb, followed by a clear bulleted list of return fields. Every sentence adds value, and the structure is easy to scan.

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 tool with no parameters and no annotations, the description is largely complete. It specifies the resource scope and the key fields returned. It lacks notes on pagination or potential large result sets, but given the existence of an output schemahol, this is a minor gap.

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?

Since there are zero parameters, the description does not need to explain parameter semantics. The baseline for 0 params is 4, and the description does not introduce any confusion or omit necessary parameter details.

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 specifies a clear verb ('List all') and resource ('serverless inference subscriptions in your Vultr account'), which differentiates it from sibling tools like inference_get_serverless_inference. The scope is explicit and unambiguous.

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 usage by stating 'List all', which suggests it is for retrieving the full set of subscriptions rather than a single one. However, it does not explicitly mention when to use this tool versus alternatives or exclude any scenarios.

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