stock_zh_a_daily

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds a valuable behavioral warning about IP banning under heavy scraping, and provides a sample source URL. This goes beyond annotations without contradicting them.

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 moderately sized and front-loads the purpose and IP warning. The parameter docstring is structured with :param/:type lines, which is easy to parse. It is slightly cluttered by the URL and docstring format, but every sentence contributes useful information.

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 parameters thoroughly and states the return type (pandas.DataFrame). However, it describes the return value only as '行情数据' (market data) without specifying columns or structure, and there is no output schema. Given the tool's complexity, this is a noticeable gap but not fatal for a historical data retrieval function.

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 input schema has 0% description coverage, but the description thoroughly explains each parameter: symbol format (e.g., 'sh600000'), date format (YYYYMMDD), and adjust options (qfq, hfq, etc.). This fully compensates for the schema gap and adds meaning far beyond mere parameter names.

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 identifies the resource: historical market data for individual A-shares from Sina Finance. The verb is implied rather than explicit (e.g., 'get' or 'query'), but the parameter list and return type make the function's purpose evident. It does not explicitly distinguish from sibling tools like stock_zh_a_hist, but the source (Sina) and daily granularity are noted.

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?

No guidance is provided on when to use this tool versus alternatives. The only behavioral caution is about IP bans from heavy scraping, which is a warning, not a usage distinction. There is no mention of preferred use cases, prerequisites, or exclusion conditions.

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