stock_ebs_lg

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, covering safety and side effects. The description adds the source URL and return type (pandas.DataFrame) but does not disclose behavior like data update frequency, whether the data is historical, or potential network dependencies. It does not contradict 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 exceptionally concise, containing only the display name, source URL, and return type in a structured docstring format. Every element serves a purpose and there is no extraneous content.

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 zero-parameter tool, the description provides the essential information (name, source, return type) but lacks context about the nature of the data, such as whether it is a time series, what columns it contains, or the period covered. Given the large number of sibling tools, a bit more context would help an agent decide if this matches the user's request.

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 tool has zero parameters and the schema is empty, so there are no parameter semantics to explain. Per the guidelines, a 0-parameter tool gets a baseline of 4, and the description correctly does not add irrelevant parameter information.

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 identifies the data source (Legulegu) and the specific metric (equity-bond spread, 股债利差), and states the return type as a DataFrame. This distinguishes it from sibling tools that cover other indicators like PE/PB. However, it lacks an explicit verb like 'retrieve' or 'fetch', relying on context.

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 given on when to use this tool versus alternatives. It does not describe scenarios, exclusions, or how it differs from related Legulegu tools such as stock_market_pe_lg or stock_index_pb_lg. The agent is left without clear selection criteria.

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