get_us_chart

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds meaningful behavioral details beyond those: default period (3mo), default interval (1d), prepost applying only to intraday, and a max row limit of 5000 with a recommendation to export large datasets. 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?

The description is well-organized with a short intro, example queries, and an Args list. It is slightly verbose due to bilingual repetition ('US stock chart OHLCV' and '미국 주식 시계열 캔들 데이터'), but every major section earns its place by adding routing or parameter value.

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 five parameters, one required, and an output schema present, the description covers all necessary invocation details: valid enum values, defaults, the intraday-only prepost behavior, and the large-data fallback. No critical information needed to call the tool correctly is missing.

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 carries the full burden for parameter understanding. It fully compensates by documenting every parameter: ticker format with example, period allowed values and default, interval allowed values and default, prepost semantics, and limit default/max. This is exactly the compensatory value needed.

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 identifies the tool as 'US stock chart OHLCV' and 'US historical price data,' making the resource and data type clear. It also distinguishes itself from siblings by positioning itself as the US time-series entry point and explicitly naming get_chart for Korean data. Example queries like 'AAPL 차트' and 'NVDA history' remove ambiguity about intended use.

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 states when to use the tool ('US 시계열 진입점') and gives concrete query examples. It also provides an explicit alternative for large data (export_us_to_excel, token 0). It does not exhaustively compare against nearby siblings like get_us_price or get_intraday_chart, but the guidance given is sufficient for common routing.

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