stock_us_daily

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

Annotations already mark the tool as read-only, idempotent, and non-destructive. The description adds valuable behavioral context by disclosing known data errors for CIEN and AI in the 复权因子 (adjustment factor), and by clarifying the meaning of each adjust value. This goes beyond annotations and informs the agent of potential data quality issues.

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 reasonably concise but somewhat unstructured; the URL and notes are embedded in a block that mixes metadata, warnings, and parameter docs. It's not excessively long, but the organization could be improved. The content is valuable, but the presentation is a bit scattered.

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 there is no output schema, the description provides sufficient context by stating the return type (pandas.DataFrame) and the meaning of the return based on adjust. The caveats about error-prone stocks add necessary context. It does not list columns or date range, but for a daily data tool, this is acceptable and the name implies the frequency.

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 thoroughly explains both parameters: 'symbol' with guidance to use get_us_stock_name to obtain it, and 'adjust' with explicit value meanings (empty string for unadjusted, 'qfq' for forward-adjusted, 'qfq-factor' for factor data). This fully compensates for the absent 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 indicates this tool retrieves US stock daily data from Sina Finance, with the name 'stock_us_daily' and the Chinese title '新浪财经-美股'. It explicitly explains the 'adjust' parameter variants (unadjusted, forward-adjusted, factor), making the resource and scope clear. While it doesn't explicitly compare to siblings, the name and content sufficiently differentiate it from other US stock tools like spot or historical minute data.

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 does not state when to use this tool versus alternatives. It only mentions that the symbol can be obtained via get_us_stock_name, which is a cross-reference but not a usage guideline. No explicit exclusions or conditions are provided.

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