athena_causal_effect_estimate

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

With no annotations, the description carries the burden and does disclose important behavioral traits: the estimates are 'assumption-scoped' and the tool 'never proves identification.' It also clarifies the supported causal strategies. It stops short of describing output details, assumptions format, or failure modes, but adds substantial context beyond the schema.

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 two concise sentences with no waste. The first sentence front-loads the action and scope, and the second provides a critical caveat. Every clause contributes necessary information.

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?

This is a complex 8-parameter tool with no output schema and no annotations, so the description needs to be thorough. It delivers a clear purpose and a key causal caveat, but it lacks an explanation of return values, parameter-method mappings, or assumptions format. It is minimally adequate for an expert but incomplete for reliable autonomous use.

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 for 8 parameters. It adds meaning by naming three method families, which implicitly maps to adjustment, instrument, and mediator, and by noting 'explicit numeric samples' for the samples parameter. Yet it does not explicitly explain the assumptions object, required combinations per method, or the roles of treatment/outcome/mediator/adjustment/instrument, leaving significant gaps.

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 opens with a specific verb ('Estimate') and clearly identifies the resource and scope: 'linear BACKDOOR, IV-Wald, or FRONTDOOR mediation effect from explicit numeric samples.' This distinguishes it from sibling tools focused on identification, discovery, or other causal targets.

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 gives clear context for when to use the tool (assumption-scoped estimation from numeric samples) and an explicit boundary: 'Estimation never proves identification,' which warns against using it for identification purposes. However, it does not name alternative tools or provide explicit 'use X instead' guidance.

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