Search Hf Models

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

No annotations exist, so the description carries the burden, and it delivers real behavioral context: results are annotated with VRAM/hardware requirements, max_vram_gb hard-filters candidates, and commercial_use applies ranking penalties rather than hard exclusion. A read-only search is self-evident from the verb; what's still unstated is ordering/pagination of the returned candidate list.

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?

Two short blocks: a one-line statement of what is searched and how results are enriched, then a tight bulleted when-to-use list. Front-loaded, no repetition of schema content, nothing wasted.

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 5-parameter filtered search with a full output schema, the description plus schema covers what an agent needs to call it. The only gap is competitive positioning against search_hf_spaces, which the description leaves to the tool names.

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 100% and each parameter already carries its own explanation, so the baseline is 3. The description reinforces meaning by tying max_vram_gb and commercial_use to concrete user situations, but adds no syntax, format, or edge-case detail beyond the schema.

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?

States a specific verb and resource ('Search for open-source AI models on Hugging Face Hub') plus an unusual differentiator: results carry hardware/VRAM requirements. The resource is distinct enough from siblings search_hf_spaces and search_github_repos that an agent can route correctly, but the description never names those alternatives explicitly.

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 'When to use' block gives concrete trigger conditions with worked examples (task types like 'automatic-speech-recognition', a max_vram_gb ceiling, commercial licensing needs). It lacks an explicit when-not or a pointer to search_hf_spaces for spaces, so the agent must infer the boundary, but the positive guidance is strong.

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