stock_lrb_em

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so safety is covered. The description adds valuable behavioral context: the date parameter format (quarter-end dates starting from 20100331) and the return type (pandas DataFrame). These details go beyond the annotations and help the agent understand the tool's behavior.

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 concise and front-loaded with the title, followed by a URL and parameter/return documentation. It uses a docstring-like structure that is efficient, though the URL and formatting could be slightly cleaner. No wasted sentences.

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?

Given the tool's low complexity (one optional parameter, no output schema), the description is relatively complete. It explains the parameter semantics, return type, and data source. It lacks column details and error behavior, but these are not critical for this simple data-fetching tool.

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%, but the description fully compensates by explaining the date parameter: allowed values are specific quarter-end dates (e.g., 20200331, 20200630), the format is YYYYMMDD, and it starts from 20100331. This adds significant meaning beyond the bare schema, which only provides a default value.

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 retrieving the income statement (利润表) from East Money's data center for annual/quarterly reports, with a specific URL and return type. This distinguishes it from sibling tools like stock_zcfz_em (balance sheet) and stock_xjll_em (cash flow statement) by specifying the exact financial statement type.

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 implies usage context via the title and parameter description but does not explicitly state when to use this tool versus alternatives. It provides no exclusions or alternative tool references, making usage guidance implied rather than explicit.

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