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

Beyond the readOnly, idempotent, and non-destructive annotations, the description adds meaningful behavioral detail: it preserves hashes, checks a latency SLA, treats file hashing as byte-only verification rather than proof of correctness, and states that parameters are processed remotely without application persistence. No contradiction with annotations exists.

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 description is front-loaded and generally economical, but it has redundancy: 'it does not generate or verify a zero-knowledge proof' is immediately restated as 'No ZK proof generation or cryptographic verification.' The remaining sentences each add distinct caveats or usage context.

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?

Given the high complexity (15 required parameters) and the presence of an output schema, the description covers the essential call semantics, safety profile, input provenance, and limitations. It could mention failure behavior when the SLA/budget check fails, but that is a minor gap given the schema and output schema.

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 100%, so the baseline is 3. The description adds useful semantic context by framing the numeric inputs as user measurements, tying the per-job latency target to the SLA field, and identifying model/input/verification-key hashes as preserved values; individual parameter formulas and units are still left mostly to the schema.

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 action and resource: it estimates proving compute and onchain verification costs from user-supplied measurements, checks a per-job latency target, and preserves hash inputs. It explicitly distinguishes itself from proof-generation tools by stating it does not generate or verify a zero-knowledge proof.

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 gives clear context for when to use the tool: when you have your own measurements and want an integration cost plan. It also names exclusions (no proof generation/verification) and points to fetch or the example resource for exact inputs, though it does not fully route among all siblings such as compare_compute_costs.

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