discover_models

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

No annotations exist, so the description must disclose behavioral traits. It mentions enriching with HuggingFace metadata, hinting at network calls, but does not explicitly state side effects (e.g., read-only, no modifications) or authentication needs beyond the optional hfToken. The description is adequate but lacks full transparency.

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 highly concise, consisting of two short sentences that front-load the main purpose and output. Every sentence is meaningful with no redundant information.

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 tool's simplicity (1 optional param, no output schema), the description adequately conveys the action and output. It mentions 'capabilities and recommended tasks' which hints at output structure, but could be improved by explicitly stating network dependence or output fields.

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?

With 100% schema description coverage, the schema already describes the optional hfToken parameter. The tool description does not add additional context about token usage or parameter semantics, so it provides no extra value beyond the schema baseline.

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 discovers local LLM models and enriches them with HuggingFace metadata, specifying the result as an enriched model list with capabilities and recommended tasks. This distinguishes it from sibling tools like llm_models (likely a simple list) and local_llm_generate (text generation).

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 usage for discovering and enriching models but does not provide explicit when-to-use or when-not-to-use guidance. No alternatives or exclusion criteria are mentioned, leaving the agent to infer usage context from sibling tool names.

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