get_score_trust

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

Annotations already mark this as read-only and non-destructive, and the description goes well beyond them by explaining the calibration verdicts, the difference between overall average calibration and per-score probability claims, the AUC-based discrimination output, and the interpretation constraints on the Priority order.

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 information-dense and front-loaded with the core purpose, but it is a single long run-on paragraph with many embedded caveats. Structuring the verdicts, rates, and discrimination guidance into separate sentences or bullets would improve scannability.

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?

With no output schema and no parameters, the description carries full responsibility for explaining the return shape. It covers the verdict values, the predicted-versus-observed rates, the discrimination AUC and interval, and the required downstream presentation caveats. Nothing essential is missing for an agent to invoke and interpret 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?

The tool takes zero parameters, so the schema has nothing to document. The description uses the no-parameter baseline appropriately by focusing entirely on what the report contains and how to interpret it.

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 states a specific verb ('report') and resource ('how well the engine's probabilities have matched reality'), and enumerates the concrete outputs: verdict, predicted-versus-observed rates, and discrimination. It is clearly distinguishable from siblings like get_posture or list_attack_paths.

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?

It gives an explicit when-to-use instruction: 'Call this before quoting any score as a probability.' It also provides when-not guidance about not presenting rankings as evidence unless priorityDiscrimination says 'discriminates', and how to handle insufficient-data results.

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