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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint. The description adds useful context: it returns an overall_hit_rate summary across the window, and clarifies the daily validation nature. No contradiction 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?

Structured with clear headings (Purpose, Triggers, When to call, Prerequisites, Next steps, Caveats, Args, Disclaimer), front-loaded purpose, and no redundant filler. The trigger phrases add value for LLM routing, and each section earns its place.

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 an output schema available, return values are covered. The description includes purpose, usage context, prerequisites, next steps, caveats, and parameter descriptions, making it complete for a simple 2-parameter getter.

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 0%, so the description's Args section is essential. It provides meaning: market_id as 'Optional market filter' and days as 'Lookback window in days (default 90).' This compensates for the lack of schema descriptions, though a bit more detail on market_id format could elevate it.

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 'Daily validation history of Level 2 structure predictions' and defines Level 2 as 'ETF / basket / sector granularity.' It specifies hit_rate per day, distinguishing it from sibling tools like get_monthly_accuracy_trend and get_structure_calibration.

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 provides usage context: 'When to call: after get_structure_calibration' and 'Next steps: get_monthly_accuracy_trend for the macro-level comparison.' It also includes trigger phrases for casual questions, guiding when to use the tool versus alternatives.

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