Estimate VRAM from a Hugging Face repo

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

Annotations already declare this a read-only, idempotent, open-world operation, so the safety profile is covered. The description adds real behavioral context beyond that: it reads config.json, may emit warnings for anything it cannot model, and always returns a full breakdown across quantisations. It does not discuss failure modes (e.g. gated/private repos, malformed configs) or latency from fetching the repo.

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 dense sentence, but front-loaded with the action and resource before enumerating outputs, and the scope qualifier (unreviewed models) plus the warnings note are placed at the end. Every clause carries information, though the output enumeration could be tightened.

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 usefully summarises what comes back (attention layout, context cost, per-quant memory, warnings), which is enough for an agent to interpret results. It omits anything about input constraints or what a warning implies for the returned numbers, but is otherwise sufficient for this tool.

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 description coverage is 100%, so repo, context, kv_cache and active_params_b are all documented in the schema, including enum values and defaults. The description adds no parameter-specific meaning beyond that; per the rubric, a 3 is the correct baseline when the schema does the heavy lifting.

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?

States a specific verb (estimates memory) and resource (a Hugging Face model repo's config.json and parameter count), and enumerates the outputs: attention layout, per-1k-token context cost, weights + KV cache + overhead per quantisation. An agent can distinguish it from estimate_vram because it works on arbitrary HF repos rather than the reviewed catalog.

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?

"For models nodegrove.io has not reviewed" gives a clear condition that routes the agent away from the sibling estimate_vram for catalog models. It does not, however, name estimate_vram explicitly or state when-not to use it (e.g. for already-reviewed models), so it stops short of full when/alternatives guidance.

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