generate_frontier

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

No annotations exist, so the description bears full responsibility. It discloses the output format and input constraints but does not mention side effects, read-only nature, error behavior, or performance implications. This is adequate but lacks explicit safety guarantees.

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 a clear first sentence, bullet-like presentation of input constraints and output explanation, and separate mention of optional overrides. No redundant sentences; every part adds value.

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?

Despite an output schema existing, the description does not cover many essential parameters (qubit_model, qec_scheme, error_budget) which are left to defaults without explanation. For a complex tool with 11 parameters, this is a significant 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 coverage is 0%, so the description must compensate. It explains the algorithm inputs and a few override parameters (qubit_model_overrides, qec_*), but leaves common parameters like qubit_model, qec_scheme, error_budget unexplained. Only about 30% of parameters get meaningful description.

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 tool generates a Pareto frontier balancing qubit count and runtime, and specifies that the algorithm must be provided via one of three input fields (algorithm_template, logical_counts, qsharp_code). It distinguishes the output as a list of optimal points, and the context of siblings like estimate_resources suggests it is for trade-off analysis.

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 instructs to provide exactly one of three algorithm inputs, mentions optional overrides similar to estimate_resources, and explains the output order (first and last points). However, it does not explicitly contrast with siblings like estimate_resources or explain when not to use this tool.

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