aurora_intervene

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

With no annotations, the description itself carries the behavioral burden and does so reasonably: it discloses the mechanism (one-variable perturbation, propagation up to max_depth hops) and the return shape (per-node deltas with confidence intervals). It does not explicitly state that data is unmodified or describe prerequisites, but the 'what-if' framing and computed-deltas wording largely convey the read-only analytical intent.

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 dense sentences with the operation front-loaded and the use case in the second sentence. Every clause adds information: mechanism, output type, usage context, and evidential basis, with no filler.

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 sparse schema with no annotations and no output schema, the description covers purpose, usage, and output type well. But it omits the meaning of the required path parameter, the role of source_entity_id, and any prerequisite like having a discovered/loaded model available, so an agent may still be uncertain about how to assemble a valid call.

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?

At 0% schema description coverage, the description must compensate. It usefully explains max_depth as 'hops' and perturbation as 'Δ', and 'perturb one variable' loosely maps to source_entity_id. However, the required path parameter is never semantically clarified, so the required parameter set is only partially documented.

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?

Clearly identifies a specific operation: what-if causal intervention on a discovered model, perturbing one variable and propagating through validated relationships. It is distinct from forecast/simulate by emphasizing 'data's own causal graph' and 'not from priors', but it does not explicitly name or contrast a sibling, so it stops short of a 5.

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?

Explicitly gives a use-case template ('what happens to Y if X changes by Δ'), which tells an agent when to select this tool. However, it offers no exclusions or named alternatives among the sibling group, leaving routing to aurora_simulate or aurora_forecast partly to inference.

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