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

Annotations already signal read-only and idempotent behavior, and the description adds meaningful operational context: results are sorted by earnings_date ascending, and is_estimate=true dates should be treated as approximate due to market data sourcing. This goes beyond the annotations and helps the agent interpret results correctly.

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 tightly organized as a bullet-point spec with the core purpose first, followed by parameter and return details. Every line adds useful information, and there is no filler or redundant fluff.

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 the parameter, the exact return envelope, field meanings, sorting order, and the reliability caveat for estimated dates. It also mentions availability. Given the provided output schema and annotations, nothing important is missing for an agent to invoke this tool and interpret its response.

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 schema already describes days fully, including its default and maximum, so the description adds little to parameter understanding. The description repeats the same semantics rather than enriching beyond the JSON Schema. A baseline 3 is appropriate because schema coverage is complete.

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 states exactly what the tool returns: 'Upcoming earnings dates for stocks in the Stocklake universe.' It adds a detailed return shape, making the resource and outcome unambiguous. This clearly distinguishes it from the sibling get_earnings_intelligence, which sounds like analysis rather than a date calendar.

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 establishes clear context for when to use this tool: any time an agent needs upcoming earnings dates within a configurable look-ahead window. The days parameter is explained with defaults and limits. It does not explicitly name alternatives or state when-not-to-use, so it misses the 5-grade bar.

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