stock_zh_index_spot_em

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

Annotations declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, and the description is consistent with these—no contradiction. The description adds useful context by specifying the return type (pandas.DataFrame) and the 'real-time' nature of the data, while omitting details like rate limits, refresh behavior, or column-structure caveats. With strong annotations covering the safety profile, the marginal behavioral disclosure is moderate.

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 four-part docstring (title, source URL, param doc, return doc) with no wasted words. The URL provides provenance, and the param/return sections are essential. The structure is standard and well-understood, though it could theoretically drop the URL without losing core semantics.

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 tool is simple (one optional parameter, no output schema), and the description covers the key aspects: data source, market scope, all parameter choices, and return type as a DataFrame. It omits DataFrame column details and data-freshness specifics, but for a snapshot-style quote tool backed by strong safety annotations, the coverage is largely sufficient.

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. It compensates excellently by enumerating all five allowed values for 'symbol' ('沪深重要指数', '上证系列指数', '深证系列指数', '指数成份', '中证系列指数'), specifying the default ('上证系列指数'), and documenting the type (str). This adds substantial meaning beyond the bare schema, which only shows a string type and default.

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 states that the tool fetches real-time spot quotes for Shanghai/Shenzhen/Beijing indices from East Money ('东方财富网-行情中心-沪深京指数' and ':return: 指数的实时行情数据'). The URL and '沪深京' scope help distinguish it from HK/global index siblings (e.g., stock_hk_index_spot_em, index_global_spot_em), and the '_em' suffix plus URL identify the East Money source. However, the differentiation is implicit rather than explicitly stated.

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. Among siblings like stock_zh_index_spot_sina, stock_hk_index_spot_em, and index_global_spot_em, there is no selection criteria or exclusionary note. The only hints are the source URL and market scope, which are implicit and not actionable.

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