zkproofport-ai

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

With no annotations provided, the description carries full burden and adds valuable behavioral context: it discloses execution time ('30-90 seconds'), server-side processing details (TEE server runs Noir circuit, builds Prover.toml), and output format (UltraHonk proof). This goes beyond basic parameter documentation to inform about performance characteristics and system behavior.

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 appropriately sized with three sentences that each add value: establishing workflow context, describing the core action with performance details, and explaining server-side processing. It's front-loaded with the main purpose and avoids redundancy, though the 'Step 3' framing could be more concise.

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 complexity of ZK proof generation, no annotations, and no output schema, the description provides good context: it explains what the tool does, performance expectations, server-side behavior, and workflow positioning. However, it doesn't describe error conditions, authentication requirements, or what the UltraHonk proof output contains, leaving some gaps for a mutation tool.

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 100%, providing complete parameter documentation in the structured schema. The description adds minimal parameter semantics beyond the schema—it mentions that inputs come 'from prepare_inputs' and that the TEE server 'builds Prover.toml from these inputs', but doesn't elaborate on parameter formats or constraints. This meets the baseline for high schema coverage.

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 the action ('submit prepared inputs to generate the ZK proof') and resource (TEE server running Noir circuit), making the purpose understandable. However, it doesn't explicitly differentiate this from sibling tools like 'generate_proof' or 'verify_proof', which appear related to proof generation/verification workflows.

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 positions this as 'Step 3 of the step-by-step flow' and references 'prepared inputs from prepare_inputs', implying usage context and prerequisites. However, it doesn't explicitly state when to use this tool versus alternatives like 'generate_proof' or 'verify_proof', nor does it provide exclusions or clear decision criteria for tool selection.

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