athena_experiment_generate

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

With no annotations, the description carries the full burden. It discloses that the tool is 'DESIGN_ONLY', implying no experiment execution or side effects, which is useful. However, it does not specify whether the operation is read-only, mutates state, requires authentication, or what the output format is, so transparency is partial.

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 a single, compact sentence that front-loads the primary action, then provides essential detail (inputs and ranking), and ends with a clear design-only flag. There is no redundancy, making it highly concise and well-structured.

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?

The tool has 6 parameters, no output schema, and no annotations. The description gives a clear high-level summary but omits details on return values, optional parameter roles, and usage constraints. It is adequate for understanding the core purpose but not sufficient for fully informed invocation.

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 0%, so the description must compensate. It adds meaning for 'factors' and 'hypotheses' by referencing 'caller-declared factor levels' and 'hypothesis factor-effect models', but it leaves cost_weight, risk_weight, sample_size, and max_candidates unexplained, providing only partial parameter semantics.

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's purpose with a specific verb ('Generate') and resource ('candidate experiments'), and details the process (from factor levels and hypothesis factor-effect models) and ranking criterion (V5 expected information gain). It is distinct from generic experiment tools, though it does not explicitly compare to sibling 'athena_experiment_design'.

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?

There is no guidance on when to use this tool versus alternatives. The sibling list includes 'athena_experiment_design' and other related tools, but the description provides no conditions, prerequisites, or exclusions, leaving the agent to infer usage context on its own.

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