list_available_models

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

With no annotations, the description carries the full burden. It discloses the return format (a dictionary of lists by category), which is useful. However, it does not state that this is a read-only operation, whether it requires network access, or if results are cached. The behavioral disclosure is minimal but not misleading.

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 crisp two-sentence summary with no wasted words. The purpose is front-loaded, and the return format is stated in the second sentence. Every word earns its place, making it highly efficient for an agent to parse.

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?

Given the tool's simplicity (no parameters, output schema provided), the description is largely complete. It explains the return structure, and the output schema covers the detailed categories. It might benefit from a hint about typical usage, but overall it suffices for a straightforward listing operation.

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 the baseline is 4. The description adds no parameter-related information, but none is needed. The schema is already fully covered, and the description correctly omits any irrelevant 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 states a specific verb ('list') and resource ('available pretrained Cellpose models'), clearly distinguishing it from siblings like segment_cells_2d and denoise_image, which perform transformations rather than enumeration. The purpose is unambiguous and immediately understandable.

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?

No guidance is provided on when to use this tool relative to alternatives. It does not mention that this should be called before segmentation to discover model names, nor does it exclude any contexts. An agent must infer usage from the name alone, which is insufficient for explicit routing.

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