generate_proof

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

No annotations are provided, so the description carries the full burden. It explains the combined behavior (one call for three steps), the required parameters per circuit, and the return value (Full ProofResult with proof bytes, public inputs, timing). It does not cover potential side effects or rate limits, but it is sufficient for typical use.

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 well-structured with clear headings for circuits and returns. Every sentence adds value, no redundancy. It is concise yet comprehensive.

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 tool's complexity (6 parameters, 3 circuits, combined steps), the description covers all necessary aspects: purpose, usage guidance, circuit-specific details, return value, and separation of concerns (use verify_proof separately). No important gaps are present.

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 baseline is 3. The description adds value by explaining circuit-specific parameter requirements (e.g., country_list and is_included for coinbase_country) and noting that scope defaults to 'proofport' if omitted. This goes beyond the schema's descriptions.

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 clearly states it is an 'All-in-one ZK proof generation' tool that combines three steps (prepare inputs, request challenge, submit proof) into a single call. It distinguishes itself from sibling tools by explicitly naming them as alternatives for fine-grained control.

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?

The description explicitly tells when to use this tool ('simplest path to a proof') and when not to ('For fine-grained control... use prepare_inputs, request_challenge, and submit_proof individually'). It also lists circuits and their required parameters, guiding the agent on which parameters to include.

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