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

With no annotations, the description carries the burden of behavioral disclosure. It does add methodological context by mentioning do-calculus and backdoor adjustment, but it does not state assumptions such as requiring a valid DAG or that all confounders are observed, nor whether the adjustment set is user-supplied or automatically found.

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?

A single sentence with no filler. The core purpose and method are front-loaded, and the parenthetical 'do-calculus' adds precision without extra bulk.

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 tool with four required parameters, no parameter descriptions, and no annotations, one sentence about method is insufficient. Key information about graph assumptions, input format, and when to select this tool over siblings is missing, though the presence of an output schema mitigates the need to describe return values.

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 mention edges, treatment, outcome, or observations. The causal terminology partly maps to the parameter names, but the description fails to explain edge format, observation shape, or how treatment and outcome are expected to be specified.

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 names a specific operation: distinguishing causal effects from spurious correlation, and adds the method (backdoor adjustment/do-calculus). This clearly separates it from broad siblings like cognitive.infer or cognitive.execute_task.

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 use case is implied: when you have a causal graph and observational data and want an effect estimate adjusted for confounding. However, it never states when not to use this tool or names alternative tools for related but different tasks.

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