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

The description goes beyond the readOnlyHint annotation by explicitly stating 'Free. No payment or wallet required' and 'This is discovery only. Sella does not host or run inference on these models.' These details add valuable behavioral context that the annotation alone does not provide.

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 concise and front-loaded with the core purpose. Every sentence adds value: scope, pricing, return contents, usage context, and limitations. There is no fluff or redundancy, making it highly efficient.

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 tool with only two simple parameters and a readOnly annotation, the description is sufficiently complete. It explains what is returned, how to use it, and what it does not do (no hosting/inference). Missing details like pagination or response format are minor given the simplicity and the description's coverage of key aspects.

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 already describes both parameters (query and limit) with full coverage. The description does not add additional meaning to the parameters themselves; it only describes the response shape. With 100% schema coverage, this meets the baseline of 3.

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 clearly states it searches the open-source AI catalogue and returns ranked artifacts with license, task, size, popularity, and links. It is specific about the resource and action, but it does not explicitly differentiate from sibling tools like search_catalog or search_datasets, so it's clear but lacks explicit 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?

It provides clear usage context with 'Use it to discover and vet an open model or dataset before wiring it into a workflow.' It doesn't explicitly mention when not to use it or name alternatives, but the intended use case is well-defined and distinguished as discovery-only.

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