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

Annotations already declare readOnly/idempotent/non-destructive/closed-world, so the safety profile is covered. The description adds genuine context beyond them: the data provenance (verified against config.json) and a data version stamp (2026-10-16), plus a preview of what the list contains. It stops short of describing pagination or size limits.

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?

A single sentence that front-loads the resource and then lists the payload. It is dense but every clause carries information; no filler or repetition.

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?

With no output schema, the description compensates well by enumerating the returned fields (id, size, attention design, native context, licence, Q4 memory at 8k context, model page). The version stamp also helps an agent judge data freshness. Only the omission of result limits/pagination keeps it from a 5.

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?

Only one optional parameter and schema description coverage is 100%, so the schema already documents 'search' fully (including examples). The description adds nothing about search behavior, which is the baseline-3 outcome when the schema carries the load.

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?

Names a specific resource (open-weight LLMs verified by nodegrove.io against config.json) and enumerates the exact fields returned, which is far more concrete than a bare 'List models'. It does not explicitly contrast itself with siblings like what_fits or can_i_run, but the catalogue framing is unambiguous.

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 contains no when-to-use guidance, no exclusions, and never names an alternative. Nothing tells the agent to reach for this tool rather than estimate_vram or what_fits, leaving routing to be inferred from the name alone.

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