athena_chance_resource_select

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 mentions 'exact only under declared finite-model assumptions,' which is a caveat about validity, but it does not disclose how the tool behaves if those assumptions are violated, what limits (like exact_limit) do, or what output to expect. This is insufficient for a complex selection/optimization tool.

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 dense sentence that is not wasteful and leads with the main action ('Select a value-maximizing small finite candidate subset'). However, its density sacrifices readability, and it omits necessary context, so it is not the ideal concise description.

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—4 parameters, nested objects, no annotations, and no output schema—the description is incomplete. It provides no parameter semantics, no usage guidance, and minimal behavioral context. The 'exact only under declared finite-model assumptions' caveat is too vague to fully prepare an agent for correct 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%, and the description does not explain any of the four parameters (candidates, budgets, alpha, exact_limit). The phrase 'independent-Gaussian one-sided resource chance constraints' hints at the meaning of budgets and alpha, but it is too abstract to help an agent construct valid arguments, and exact_limit is not mentioned at all.

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 'selects a value-maximizing small finite candidate subset' under specific independent-Gaussian chance constraints, which distinguishes it from sibling tools like athena_dro_resource_select by naming the exact optimization model and objective. The verb 'select' and the resource 'candidate subset' are specific, and the constraint type (one-sided resource chance constraints) adds differentiation.

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 implies usage for problems involving independent-Gaussian chance constraints, but it does not explicitly state when to use this tool versus alternatives such as athena_dro_resource_select or athena_two_stage_resource_plan. No exclusions, prerequisites, or alternative tool names are given, so the usage guidance is only implied.

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