Get accuracy heatmap

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the agent knows it is a safe read operation. The description adds useful context about the default recent window when dates are omitted, but does not disclose response dimensions or data completeness. No contradiction with annotations. This matches the calibration example where annotation-covered tools get a baseline of 3.

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 sentences, front-loaded with the core purpose, and contains zero waste. It efficiently packs the tool's function, use case, and date format guidance into a compact form.

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 only 2 optional parameters, no output schema, and strong annotations, the description is sufficiently complete. It explains what the heatmap shows (hit-rate over time/ticker), when to use it, and how to specify dates. The agent has all necessary information to decide when to invoke and how to pass parameters 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 input schema already provides a pattern and description for YYYY-MM-DD dates with 100% coverage. The description goes beyond the schema by explaining the default behavior when dates are omitted ('omit for the default recent window'), which clarifies parameter optionality and default values. This adds meaningful semantics beyond the structured fields.

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 the tool produces a 'Hit-rate heatmap of signals over time/ticker', specifying both the resource (signals) and the output form (heatmap). It further clarifies meaning with 'which signal types worked when', distinguishing it from sibling tools like stoquant_get_signal_accuracy which likely returns a single accuracy value.

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 gives an explicit use case: 'Use for backtest-style "has this signal been reliable lately" questions.' This provides clear contextual guidance. However, it does not explicitly name alternatives or state when not to use it, so it falls one step short of a top score.

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