Get platform accuracy summary

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

Annotations already indicate readOnly and openWorld, but the description adds valuable behavioral context: the benchmark preference (IWM/^RUT with SPY fallback), the meaning of spreadPp as 'the real edge', and inclusion of a Wilson confidence interval and dataSufficiency flag. This goes well beyond annotation hints.

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 a single dense sentence that front-loads the purpose and packs many details efficiently. While it is somewhat long and requires careful parsing, it contains no fluff and every clause adds useful information.

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 parameters and no output schema, the description carries the full burden of explaining the return value. It thoroughly enumerates the fields, clarifies the benchmark fallback logic, and explains the significance of spreadPp and dataSufficient, making it effectively self-contained.

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 has zero parameters, so no parameter descriptions are needed. Per the rubric, a baseline of 4 is appropriate when there are no parameters and the schema provides full coverage.

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 begins with 'Platform-wide model scorecard' and enumerates the specific metrics returned, making it clear this is the aggregate accuracy summary for the whole platform. This distinguishes it from sibling tools like stoquant_get_signal_accuracy, which are more narrowly scoped.

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?

The description explicitly states 'Use to gauge overall reliability of StoQuant's signals right now,' giving a clear use case. It does not explicitly mention when not to use it or name alternative tools, but the platform-wide scope makes usage context sufficiently clear.

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