stock_market_pb_lg

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

Beyond the read-only and idempotent annotations, the description adds the data source URL and declares the return type as pandas.DataFrame. It does not disclose potential rate limits, data granularity, or how the DataFrame is structured, but for a safe read tool the annotations already cover the main behavioral contract. No contradiction with 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 four lines: title, source URL, parameter spec, and return spec. It is appropriately sized and front-loaded, with no filler. The structure is clear even though the language is Chinese.

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 single-optional-parameter tool with no output schema, the description provides the necessary parameter choices and return type. However, it leaves ambiguity about the nature of the returned data (e.g., time series vs. snapshot, column names, frequency). This is a clear gap, though the tool is simple enough that the agent can likely infer usage.

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?

With schema description coverage at 0%, the description is the only source of parameter meaning. It explicitly enumerates the valid values for 'symbol' (上证, 深证, 创业板, 科创版), which is essential for invoking the tool correctly. This adds crucial semantics that the JSON schema entirely lacks.

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 title and description explicitly state this returns P/B (市净率) data for a selected market (指定市场), with the URL indicating the source. It uses a clear resource and scope, distinguishing it from PE-focused siblings like stock_market_pe_lg. However, the verb (get/retrieve) is only implied via the return type, and the title mentions 'main board' while the symbol options include non-main-board markets, slightly muddying clarity.

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 offers no guidance on when to use this tool versus alternatives like stock_index_pb_lg or stock_market_pe_lg. It does not state prerequisites, exclusions, or scenarios. An agent would have to infer usage from the name and parameter choices.

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