List Nano Banana models

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

No annotations are supplied, so the description carries the burden, and it does so by disclosing that this is a fixed enumeration of three models and enumerating the attributes returned. It does not state read-only safety explicitly or the exact response shape, but for a zero-parameter lister side effects are not plausible.

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?

A single sentence, front-loaded with the resource, then the payload contents. Every clause carries distinct information with no filler.

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?

With no output schema, the description must convey the return contents, and it does so thoroughly (aliases, resolution, thinking_level, grounding, reference limits, auto-selection eligibility). Only the literal response structure/field names are left unspecified, which is a minor gap for a catalogue tool.

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?

Zero parameters, so the baseline of 4 applies. The description adds no parameter detail because there are none to describe, and the schema confirms an empty object.

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?

Specific verb (returns) plus resource (the three supported Gemini image models) and an explicit inventory of what comes back: aliases, capabilities, resolution, thinking_level, grounding, reference limits. This clearly separates it from generate_image/edit_image, which act on images rather than enumerate models.

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?

Usage is implied rather than stated: an agent can infer this is the discovery call to run before generate_image/edit_image to learn model names and capabilities. There is no explicit when-to-use, when-not-to-use, or named alternative, so it stays at the implied level.

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