hf_build_index

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

With no annotations, the description carries the full burden. It discloses the full pipeline (fetch, extract, store), the additive behavior, and clarifies that the `force` parameter is unused. This provides good transparency for a mutation tool.

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 front-loaded with the main purpose and pipeline summary. The parameter documentation is structured but slightly verbose. Overall, it efficiently conveys key information without excessive wordiness.

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?

Given the complexity (6 params, no output schema details, sibling diversity), the description adequately covers behavior and parameters. It lacks explicit mention of return values, but an output schema exists to provide that.

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?

Despite 0% schema description coverage, the description's docstring explains every parameter in detail, including relationships (e.g., `task` vs `category`), default behaviors, and source-specific constraints (e.g., `min_likes` only for HuggingFace). This adds significant value 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?

The description clearly states the tool's purpose: 'Fetch models and add them to the semantic network.' It details the extraction pipeline and distinguishes this from sibling tools focused on searching or retrieving single models.

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 notes the additive nature ('multiple calls enrich the same network'), which indicates usage context. However, it does not explicitly state when not to use this tool or provide alternatives, leaving the agent to infer usage boundaries.

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