atlas_uncertainty

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

With no annotations, the description partially covers behavioral aspects by listing what it computes (agreement, drift, confidence). It does not disclose side effects, authorization needs, or data freshness, leaving some ambiguity about the tool's behavior beyond being a query.

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 two concise sentences with no fluff, front-loading the core purpose and usage guidance. Every phrase adds value.

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 low complexity (one required param, no output schema), the description sufficiently enumerates the tool's outputs (cross-model agreement, calibration drift, confidence diagnostics) and usage, making it complete for its scope.

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 one parameter country_code already described as an ISO code. The description adds no extra semantics beyond confirming it works 'for a country,' so it meets but does not exceed the baseline.

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 provides 'prediction uncertainty for a country' including specific diagnostics like cross-model agreement and calibration drift. It distinguishes itself from sibling tools such as atlas_score and atlas_prediction_track_record by focusing on trustworthiness metrics.

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?

Explicitly says 'Use to gauge how much to trust a prediction,' providing clear usage context. However, it does not mention when not to use this tool or suggest alternatives among similar sibling tools like atlas_prediction_track_record.

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