athena_longitudinal_gformula

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the method but does not mention side effects, required assumptions in detail, what the output looks like, or any constraints on inputs. 'Assumption-scoped' hints at assumptions but is too cryptic to be informative.

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 concise and front-loaded with the key action ('Estimate'). The second sentence, 'Assumption-scoped,' is compact but vague, arguably not earning its place since it adds incomplete information. Overall, it wastes few words but sacrifices clarity for brevity.

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 (8 parameters, nested objects, no output schema, no annotations), the description is too minimal to be complete. It does not describe return values, assumption requirements, sample data structure, or how to specify regimes. This leaves significant gaps for an agent deciding whether and how to invoke it.

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?

The schema has 8 parameters with zero description coverage in the schema. The tool description compensates only partially: it clarifies the role of 'intermediate' (binary) and implies 'treatment1'/'treatment2' correspond to time points, but leaves 'samples', 'regimes', 'baseline', and 'assumptions' unexplained. This is insufficient for an agent to confidently construct parameters.

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 function: 'Estimate static two-timepoint treatment-regime risks' via a 'transparent parametric g-formula.' It also specifies the scope (over one binary intermediate variable), which distinguishes it from related longitudinal tools like athena_longitudinal_tmle, which use different estimation approaches.

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 provides no explicit guidance on when to choose this tool over alternatives. The phrase 'Assumption-scoped' is vague and does not explain which assumptions (e.g., no unmeasured confounding, positivity) make this approach appropriate, nor does it name alternative tools for different scenarios. Without this, an agent lacks clear direction on use cases versus siblings.

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