get_earnings_this_week

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

Annotations already declare readOnly=true, openWorld=true, idempotent=true, and destructive=false. The description adds valuable context beyond that: cache-first behavior with a fallback to live fetch under specific conditions (#523), enrichment with company name and 33-sector data, and supported plans. This is exactly the kind of behavioral clarification that helps an agent predict side effects.

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-organized into clear sections: purpose, use cases, defaults, enrichment, alternative, plan/cache note, and args. It is front-loaded with the core function, and every sentence earns its place without redundancy. The length is appropriate for the amount of behavioral detail provided.

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 that an output schema exists, the description does not need to explain return values. It covers essential contextual aspects: supported plans, caching fallback behavior, data enrichment, and parameter defaults. The tool is simple (2 optional params, read-only, list-style), and the description leaves no critical gaps for invocation or interpretation.

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 coverage is 0%, but the description fully compensates with an explicit Args section. It details both parameters with inclusive bounds, accepted formats (YYYYMMDD or YYYY-MM-DD), and defaults (today for date_from, date_from+7d for date_to). This is far more informative than the bare schema, which only shows string/null types.

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 opens with a specific verb-resource phrase: 'Companies reporting earnings in a date window, grouped by day', and the Japanese '今週の決算予定' clarifies the weekly scope. It distinguishes from sibling get_equities_earnings_calendar by explicitly noting that tool is for a single stock's next earnings date.

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?

It lists concrete use cases in both Japanese and English ('今週決算がある銘柄', 'earnings this week') and explicitly points to an alternative for single-stock queries: 'For a single stock's next earnings date use get_equities_earnings_calendar(code=...).' This gives clear when-to-use and when-to-use-otherwise guidance.

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