search_ai_models

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

With no annotations provided, the description fully bears the transparency burden. It discloses key behaviors: the search is free and requires no key; the search returns only a subset of metadata and full records must be purchased via get_record; and it indexes 'factual repository metadata only' without reproducing model card text. These limitations are clearly stated.

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 structured in two clear parts (overview + Args) and front-loads the most important fact ('FREE, no payment and no key'). However, it repeats 'free' twice ('FREE, no payment and no key' and 'Free to search') and is slightly longer than necessary, though every sentence still adds some value.

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 one parameter and no output schema, the description is remarkably complete: it lists all return fields, explains the paywall limitation, provides usage context, and notes the restriction to factual metadata. It even mentions the relationship to get_record, making the overall workflow clear.

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 only parameter, `query`, is given a plain type in the schema but the description adds meaningful semantics: 'keyword(s) to search' and examples such as 'image segmentation', 'nvidia', or 'apache-2.0'. This goes beyond the basic schema and helps the agent formulate valid queries.

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 ('Search') and clearly identifies the resource ('AI and machine learning models on the Hugging Face Hub') along with search dimensions ('by name, author or organization, task, library, or license'). It also distinguishes itself from sibling search tools by naming its domain and enumerating the exact fields returned.

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 explicitly states when to use this tool: 'For model selection, license screening before adoption, and dependency review.' It also clearly directs users to an alternative tool: 'call get_record with a record_id to buy the full metadata record,' providing a when-to-use vs. when-to-move-on contrast.

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