roboflow_pretrained_list

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

With no annotations, the description must carry the behavioral burden. It conveys a read-only list operation through the verb 'List,' but adds no details about response format, pagination, or any limitations. The 'production-ready, zero-setup' phrase describes the endpoints, not the tool's behavior, so it adds little transparency.

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 two sentences, front-loaded with the verb and resource. Every clause adds useful context with no redundancy or 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?

The tool is simple with one optional parameter and no output schema. The description sufficiently conveys the tool's purpose and scope. It could mention that it returns a list of models, but that is strongly implied by 'List.'

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 input schema fully covers the single 'task' parameter, including enum values and a description. The tool description adds no extra parameter meaning, so the baseline 3 is appropriate.

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 uses a specific verb 'List' with a clear resource: 'Roboflow's curated pre-trained APIs.' It also provides examples of vision tasks (OCR, people detection, PPE, license plates), distinguishing it from sibling tools that list user projects or search the universe.

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 for retrieving ready-made, zero-setup vision APIs, but it does not explicitly state when to prefer this over roboflow_universe_search or roboflow_project_list, nor does it mention any exclusions.

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