stock_pg_em

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds the source URL and return type (pandas DataFrame), providing some value, but it does not disclose other behavioral traits such as data freshness, rate limits, or scope of the returned data.

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 very concise and front-loaded with the source and return type. It follows a docstring-like structure (return and rtype) without unnecessary prose. It could be slightly more informative, but it is appropriately sized for a zero-parameter tool.

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 simplicity (no parameters, no output schema), the description is minimally adequate. It states the source and return type, but it does not describe the columns or the exact nature of the '配股' data, leaving some ambiguity about the returned DataFrame's contents.

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 takes zero parameters, and the schema covers 100% of that (none). With no parameters to describe, the baseline is 4, and the description does not need to elaborate on parameter semantics.

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 resource (配股/allotment data from Eastmoney) and indicates it returns a pandas DataFrame. Although it lacks an explicit verb like 'get' or 'fetch', the URL and return type imply data retrieval, and it is distinguishable from sibling tools focused on IPOs and other new-stock 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 alternatives. It does not mention any exclusions, prerequisites, or related tools, leaving the agent without context on selection.

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