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

Annotations already establish readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds real value beyond that by disclosing that the peak is a prediction with a margin and a derivation breakdown, and that it cross-references a measured probe when one exists with matching settings — telling the agent the estimate's provenance and reliability.

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?

Two tight sentences: the question comes first, then the return contract. No filler, and the most decision-relevant information is front-loaded.

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 10 parameters and no output schema, the description does the important work of enumerating the return shape (verdict values, peak MB, margin, derivation, probe). What remains thin is parameter-level meaning for the LoRA config fields, but the core call semantics are covered.

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 60%, so roughly half the parameters (rank, model, dora, lora_dropout, other_processes_mb) carry no descriptive text anywhere. The description only echoes vram_gb, which the schema already documents. It adds almost no parameter meaning beyond the structured fields, so the 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 frames a concrete feasibility question ('Will this LoRA config train on a card with vram_gb GB?') and enumerates the outputs (verdict, peak MB, margin, derivation, probe). This makes the tool's role as a yes/no VRAM estimator unambiguous. It does not explicitly differentiate itself from vram_max_rank or vram_probes, though the mention of 'any measured probe with the same settings' hints at the relationship.

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?

Usage is only implied by the yes/no question framing — an agent can infer it is for checking a specific config against a specific card. There is no explicit when-to-use guidance, no statement of when to prefer vram_max_rank or vram_probes instead, and no prerequisites mentioned.

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