get_ai_performance

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds behavioral context by specifying that the data is forward-test (not backtest), which informs the agent about the nature of the metrics. No contradictions with annotations.

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: two sentences (plus Vietnamese translation) that front-load the purpose and key metrics. Every word adds value, with no wasted space.

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 zero parameters and no output schema, the description adequately covers the tool's return value (specific performance metrics) and data type (forward-test). It could mention update frequency or time range, but this is not critical given the sibling tool for reports likely provides more detail.

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 has no parameters, so the description carries the full burden of explaining what the tool returns. It lists specific metrics (win rate, average return, Sharpe ratio) and clarifies the data type (forward-test), adding meaning beyond the empty schema.

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 verb 'Get' and the resource 'AI stock pick performance metrics', listing specific metrics (win rate, average return, Sharpe ratio). It distinguishes from the sibling 'get_ai_performance_reports' by implying this is a summary vs. detailed reports, and clarifies it's forward-test data, not backtest.

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 notes 'Forward-test data (not backtest)', guiding agents on when to use this tool for real performance metrics vs. backtest data. However, it does not explicitly mention alternatives like 'get_ai_performance_reports' for more detailed reports, though the context and naming suggest differentiation.

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