athena_sequential_dr_policy_value

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

With no annotations, the description carries the full burden. It discloses methodological behavioral traits (AIPW augmentation, history preservation, assumption scope) but does not mention operational side effects, output shape, validation behavior, or failure modes.

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?

Two sentences, front-loaded with the main purpose, followed by a scope warning. Every word serves a purpose and there is no redundancy.

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?

For a complex tool with no annotations, no output schema, and 0% parameter coverage, the description is insufficient. It does not explain input structures, assumption object requirements, output format, or how it differs from the crossfit sibling, leaving the agent under-informed 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 has 0% description coverage. The description provides only high-level hints (two-timepoint, policies) but does not define or elaborate on any of the nine parameters such as 'samples', 'policies', 'assumptions', or 'propensity_clip'.

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 uses a specific verb-resource pair: 'Estimate deterministic two-timepoint dynamic-policy value' with methodological qualifiers ('sequential AIPW augmentation', 'explicit history preservation'). This distinguishes it from siblings such as the crossfit variant and more general longitudinal tools.

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?

It provides an important caveat: 'Assumption-scoped; not a general longitudinal causal theorem.' This implies when it should be used (when assumptions hold) and warns against general use, but it does not explicitly name alternatives or provide concrete selection criteria versus the crossfit sibling.

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