search_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 that the search is free-text, that task maps to HF pipeline tags, that sort_by controls ordering, and that limit is capped at 50. These details go beyond the schema alone escal and give an agent confidence about how the tool behaves.

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 clear summary sentence is followed by a compact, alphabetically organized argument block. No filler or repetition; every line adds information that does not appear in the schema.

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 read-only search tool with an output schema presentholer, the description covers all inputs, including default sort and task examples. It lacks nothing essential for making a correct call. The presence of sibling tools in context strengthens understanding, though not explicitly referenced in the description.

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?

Schema description coverage is 0%, so the description compensates fully. Each parameter is explained with available values and concrete examples, including the hidden limit constraint (1-50) and the meaning of the enum values. This is exactly the added value needed.

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 opening sentence states a specific verb+resource combination: 'Search Hugging Face Hub for models matching a query'. It also lists optional filters (task, sort), which immediately distinguishes it from sibling tools like get_model_details or compare_models that operate on specific models rather than discovery.

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 clear usage context: it is for free-text discovery of Hugging Face models with optional filtering and sorting. It doesn't explicitly name sibling tools or exclusion conditions, but the task-oriented examples (e.g., 'llama instruct' or 'sentiment analysis') make the intended use obvious.

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