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

Annotations already declare readOnlyHint=true and openWorldHint=false, so a pure local computation is understood. The description usefully explains the two-tier semantics (fits-with-margin vs tight), which is real added context, but it does not disclose the output shape or the cost/assumptions of the probe-estimate it describes.

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 dense sentence with the operative concept (largest rank) front-loaded and the two-tier clarification immediately after. No filler, though the nested parentheticals make it slower to parse than necessary.

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 and 8 parameters, the description should at least characterize the return value's structure (two ranks? a dict? units?). It conveys the concept fully but leaves the response shape and the dependence on model/quant/seq_len unstated.

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 63%; vram_gb's meaning is already in the schema, and the description adds only the 'GB as sold' framing. The other seven parameters (model, quant, seq_len, dora, dropout, response_len, other_processes_mb) that materially change the answer are never referenced in the description.

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 states a specific derived quantity (largest LoRA rank, multiple of 8) as a function of vram_gb, and even returns two tiers of answer rather than one. It does not name or contrast with the siblings vram_fit or vram_probes, so differentiation is left to inference.

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?

Use is implied (planning a LoRA config for a given card size), and the parenthetical about 'a real run may fit, the one-step probe would not' hints at the relationship to vram_probes, but it never says when to call this instead of vram_fit or vram_probes.

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