stock_zh_index_daily_tx

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safe-read nature is clear. The description adds valuable behavioral context beyond annotations: all data is '前复权' (forward-adjusted), different websites use different adjustment methods, and the data source is Tencent Securities. This helps the agent understand adjustment implications without being told explicitly in the schema.

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 title, rationale, warning, URL, and parameter docs. Each sentence serves a purpose. There is slight redundancy between the title and the first sentence, but overall it remains efficient and scannable for an agent.

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?

For a 3-parameter read-only tool with no output schema, the description covers the source (Tencent Securities), adjustment behavior, parameter formats, and provides a reference URL. It states the return type as a pandas DataFrame, but does not enumerate columns; however, that gap is minor given the tool's simplicity and the presence of a link for more details.

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?

Although schema description coverage is 0%, the description provides comprehensive parameter documentation in the docstring: symbol is a stock/index code with market identifier, start_date is in YYYYMMDD format and defaults to the earliest date when empty, and end_date similarly defaults to the current date. This fully compensates for the lack of schema descriptions and gives the agent precise input formatting guidance.

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 tool as fetching daily-frequency historical data for stocks or indices from Tencent Securities, with a specific verb+resource structure. It explicitly distinguishes itself as a supplement to ak.stock_zh_index_daily() for cases where Sina data is missing, making it easy to understand what this tool does relative to siblings.

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 provides a clear use case: it serves as a fallback for missing index data on Sina, as stated in '作为 ak.stock_zh_index_daily() 的补充, 因为在新浪中有部分指数数据缺失'. It also warns that data is forward-adjusted and should not be mixed with other sources, which is important usage guidance. It does not explicitly enumerate alternatives, but the referenced complement function serves that role.

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