stock_intraday_em

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so safety is covered. The description adds a return type (pandas.DataFrame) and a reference URL, but does not disclose behavioral details such as the DataFrame's columns, time granularity, intraday period covered, or any rate limits. This is minimal added value beyond the 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 a compact docstring with only essential lines: title, URL, param, type, return, rtype. It avoids lengthy prose and each line serves a purpose. However, the lack of a main sentence makes it feel more like metadata than a human-readable explanation, preventing a score of 5.

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?

With one parameter and no output schema, the description should explain what the returned DataFrame contains, the intraday interval, and any data limitations. It only states '分时数据' (intraday data) and 'pandas.DataFrame', leaving the agent uncertain about columns, time range, and whether it covers real-time or historical data. The URL is not explained either.

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 coverage is 0%, so the description must compensate. It defines 'symbol' as 股票代码 (stock code), which is a translation of the parameter name but adds little meaning. It does not explain the expected format (e.g., exchange prefix, leading zeros), even though the example URL suggests a market prefix ('0.000001'). The default '000001' offers some hint, but the description fails to clarify edge cases.

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 names the source (东方财富/Eastmoney) and data type (分时数据/intraday data), which identifies the tool's core function of fetching intraday stock data. However, it lacks a clear verb (e.g., 'retrieves', 'gets') and does not differentiate this tool from similar sibling tools like stock_intraday_sina or stock_zh_a_hist_min_em, which also deal with intraday/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?

There is no guidance on when to use this tool versus alternative data sources or tools. The description simply lists a parameter and return type without any contextual usage instructions, prerequisites, or exclusions. The agent receives no help in deciding whether to invoke this tool over its many siblings.

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