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

Annotations already mark this as read-only. The description adds useful result content (accuracy and quality-gate status), but it doesn't disclose return shape, sorting, pagination, or default behavior beyond what the schema already says.

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, front-loaded sentence conveys the core data without filler or repetition. It wastes no token budget.

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 simple read-only tool with only two optional parameters, the description plus schema covers the essential call semantics and the nature of the returned data. It would be more complete if it explicitly described the result as a ranked list, but nothing critical is missing for a basic call.

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 100%, so the description is not required to re-explain limit or symbol. It doesn't add any extra parameter meaning, matching the baseline for fully documented schemas.

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 concrete resource—historical accuracy of forecasting models plus a quality-gate flag—which is far more specific than the tool name alone. It doesn't explicitly compare against sibling tools like get_forecast or get_signal_prediction_performance, so it misses the explicit differentiation needed for a 5.

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 statement of when to use this tool instead of alternatives is provided. The optional symbol and limit parameters imply a filtering use case, but the description neither states it nor excludes any sibling tools.

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