List models

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint=false and destructiveHint=false, so the safety profile is covered. The description adds genuinely useful context beyond that: a dataset version stamp (2026-09-25) signaling data freshness and the basis of verification (config.json), letting the agent judge staleness.

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?

One front-loaded sentence with the resource stated first and the returned fields enumerated compactly. It is dense but with no filler sentences; the colon-delimited field list is efficient.

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?

No output schema exists, but the description compensates by enumerating the returned fields, which is the key completeness requirement for a list tool. It omits catalog scale or pagination behavior, a minor gap for a 0-required-param read-only listing with full annotation coverage.

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 coverage is 100% and the single 'search' parameter already carries its own description with examples ('qwen', '24 GB'). The tool description says nothing about the filtering parameter, so it adds no meaning beyond the schema; baseline 3 applies.

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 names a specific resource (open-weight LLMs verified by nodegrove.io) and enumerates the returned fields (id, size, attention design, context, licence, memory at Q4/8k, page), so an agent knows exactly what data it gets. It does not explicitly differentiate itself from siblings like estimate_vram or what_fits, but the resource is clearly distinct.

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?

There is no statement of when to call this versus the estimation siblings (estimate_vram, estimate_from_hf_repo, can_i_run, what_fits). Usage is only weakly implied by the catalog-listing nature of the resource, and no prerequisites or exclusions are given.

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