athena_causal_identify

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

With no annotations, the description carries the transparency burden. It discloses that identification is conditional on supplied graph/assumptions and uses d-separation, but it does not describe the return format, whether multiple sets are possible, or any side effects. Moderate disclosure for a computation 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?

Two concise sentences with no redundancy. The main action is front-loaded, and every word serves a purpose.

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 no annotations, no output schema, and 7 parameters including nested objects, this brief description is insufficient. It does not explain return values, parameter details, or usage alternatives, leaving significant gaps for an agent to invoke the tool correctly.

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 provides only high-level context (supplied DAG, assumptions) without detailing parameters like observed_nodes or max_adjustment_size. The word 'minimal' hints at max_adjustment_size but does not explain semantics or syntax.

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 'Search' and resource 'minimal back-door adjustment set in a caller-supplied causal DAG', clearly stating the tool's function. It also differentiates from siblings like athena_causal_identify_extended by emphasizing the minimal constraint.

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 use when a caller has a causal DAG and wants a minimal adjustment set via d-separation, but does not explicitly state when to choose this over alternatives or provide when-not-to-use exclusions. The context is clear but alternatives are not addressed.

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