validate_predictions

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

No annotations are provided, so the description carries full burden. It states the operation ('fetch and validate') but does not disclose side effects, idempotency, error conditions, or behavior under edge cases (e.g., what happens if predictions are already validated).

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 extremely concise, consisting of two short sentences and an Args block. It is front-loaded with the core purpose, contains no fluff, and every phrase earns its place.

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?

Given the tool has an output schema (not shown), the description need not detail return values. It covers the input and high-level operation adequately. However, it might hint at the validation result type (e.g., pass/fail, scores) to improve completeness.

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 has 0% coverage for parameter descriptions, but the tool's description defines 'current_state_summary' as 'a summary of the current reality/system state to test predictions against', adding meaningful context beyond the schema's type and title. However, it could specify expected format or examples.

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 it fetches unresolved predictions and validates them against current state. The verb 'validate' and resource 'predictions' are specific, but it does not distinguish from sibling tools like 'predict', 'calibration_resolve', or 'resolve_hypothesis', which may overlap in domain.

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 versus alternatives such as 'calibration_resolve' or 'resolve_hypothesis'. The description only implies usage when predictions exist to validate, with no exclusions or context for when not to use.

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