premortem

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

With no annotations, the description carries the full burden, and it does disclose two useful traits: the return includes edge cases, and a 'trustworthy' flag is set to False when there is no hit history (anti-theater guard). It does not cover permissions, determinism, or how the ledger influences trust, leaving real gaps.

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 dense sentence with the purpose front-loaded and no filler, which is appropriately compact for a computational tool. The terseness comes at the cost of some missing detail rather than being wasteful.

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 no annotations, no output schema, and 0% parameter coverage, the description does mention outputs (expected outcome, edge cases) and the trust flag. However the ledger input and the tool's relationship to adjacent decision/prediction tools remain undefined, so it is only partially complete.

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 compensate. It usefully documents the shape of the scenarios entries ([{label,value,prob}]), but the 'ledger' parameter is never mentioned, so compensation is only partial.

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 function: it takes scenarios [{label,value,prob}] and produces an expected outcome plus edge cases, which is a concrete verb+resource transformation. It clearly conveys what the tool computes, though it does not name or contrast any sibling tool.

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?

There is no statement of when to use this tool versus alternatives such as prediction_calibration, situation_stress_test, or the calibrated_consult family. The only conditional note (trustworthy=False without hit history) is a behavioral property, not routing guidance.

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