athena_transition_distribution

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

With no annotations, the description must convey behavioral traits. It discloses the statistical shrinkage behavior and empirical basis, but omits potential prerequisites (e.g., minimum row count) and whether the operation is read-only or has side effects.

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 a single, jargon-dense sentence that front-loads the core action and output. No wasted words, though some terminology may be opaque.

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 complex multivariate statistical tool with no output schema and no annotations, the description is incomplete. It does not describe the exact return structure (e.g., mean vector and covariance matrix orientation), required data prerequisites, or how context influences the result.

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 explain parameters. It hints at action_id via 'action-conditioned' and prior_strength via 'shrinkage' but leaves the context object entirely unexplained.

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 clearly states the tool's function: returning a multivariate empirical action-conditioned transition mean/covariance. It uses specific technical language that distinguishes it from sibling tools like transition_predict or transition_observe by emphasizing observed rows and shrinkage.

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 provides no explicit guidance on when to use this tool versus alternatives. It only implies use with observed transition data ('from V5 observed transition rows') but does not mention sibling tools or when to choose this over prediction/observation tools.

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