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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, so the safety profile is covered. The description adds valuable behavioral context: the tool returns empty results until structure-learning cycles complete, and it clarifies the Level 2 granularity (ETF/basket/sector) versus Level 1 individual symbols, which is useful for expectation setting.

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 well-structured with labeled sections (Purpose, Triggers, When to call, Prerequisites, Next steps, Caveats, Args, Disclaimer) and is front-loaded with the purpose. It is slightly longer than strictly necessary due to the inclusion of multiple trigger phrases and a disclaimer, but 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?

The description covers all essential context: purpose, usage triggers, prerequisites, next steps, caveats, and parameter semantics. Since an output schema exists, the lack of return-value description is acceptable. The description is complete for the tool's complexity and configuration.

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?

Despite zero schema description coverage, the description provides meaningful semantics for both parameters: market_id is an optional market filter with specific values (crypto, kr_stock, us_stock), and group_name is an optional group/sector filter with examples (layer1, defi, sector, broad_index). This compensates fully for the lack of schema descriptions.

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's function: 'Returns hit_rate_ema per (market, group, interval, regime_bucket) with sample counts' and explicitly positions it as Level 2 (ETF/basket/sector) calibration, distinguishing it from Level 1 individual symbols. It also differentiates from siblings like get_structure_validation_history by mentioning it as a next step.

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?

Provides explicit 'When to call' guidance (Layer D evidence), example trigger phrases ('how good are your sector calls?'), prerequisites (none), and next steps (get_structure_validation_history for the daily trend). This goes beyond mere context to actionable usage criteria.

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