predict_outcome

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

No annotations provided, so description must disclose behavioral traits. It only mentions delegation to CausalReasoner but does not state if it is read-only, what it returns, or any side effects. Critical details like output format or idempotency are absent.

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?

Single sentence plus internal delegation note. Efficient but includes a versioning prefix ('3B.7 —') that adds little for an agent. Overall concise.

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?

No output schema, and description does not hint at return format or how predictions are presented. Lacks context relative to many sibling causal tools. Incomplete for effective agent decision-making.

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 coverage is 100% with clear descriptions for both parameters. Description adds minimal value beyond schema, mainly clarifying the causal context. Baseline 3 is appropriate.

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?

Description clearly states it predicts downstream effects by walking causal subgraph. Verb 'predict' and resource 'downstream effects' are specific. Implicitly distinguishes from siblings like find_effects by mentioning action and candidates parameters, but no explicit differentiation.

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?

No guidance on when to use this tool vs alternatives. Sibling list includes find_effects and counterfactual_query, but description offers no context for selection or prerequisites (e.g., existence of a causal model).

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