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

With no annotations, the description carries the full behavioral burden and does well: it discloses auth requirements (Bearer key), per-call cost and pricing range, the return categories, and the key caveat that estimates can never prove fit. It even states the underlying endpoint being proxied. It stops short of describing rate limits or failure behavior, but the disclosure is unusually strong for an unannotated tool.

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?

The purpose is front-loaded and there is little outright filler, but the middle sentences are terse to the point of opacity ('Uses basis full with sourced headroom'), and the pricing/endpoint/auth material is packed densely into the same block. It is compact yet sacrifices readability.

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 properly explains the returned verdict types (measured/estimated/undetermined), and it covers auth and cost. But for an 8-parameter tool with 0 required fields, it leaves most input semantics unexplained in both description and schema, which is the main completeness gap.

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 only 13% (essentially just 'memory'), and seven of eight parameters (sku, model, context, runtime, device_count, quantisation, batch) are undocumented in both schema and description. The description adds only 'Memory is decimal GB per device, never pooled,' which is a modest clarification beyond the schema's own text and does not compensate for the wide coverage gap.

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 opens with a clear question ('Does this workload fit?') and enumerates the three verdicts it returns (measured/estimated/undetermined), which communicates both verb-adjacent intent and scope. It differentiates from siblings indirectly with 'It doesn't choose a model,' steering the agent away from decide/rank/compare_models, though it never names those tools.

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?

It provides conditional guidance ('basis lower_bound for unlikely only', 'Missing headroom cannot establish likely'), which implies when certain bases are appropriate. However, it never states when to choose this tool over policy_check, decide, or rank, nor what inputs are required versus optional, leaving routing to inference.

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