fund_etf_scale_szse

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds only the source URL and the fact that it returns a DataFrame. It does not disclose any additional behavioral traits such as data freshness, update frequency, size limits, or whether it includes historical data or only current shares. The minimal addition leaves the agent without useful behavioral context beyond the 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 very short (three lines) and includes the title, a URL, and return type. It is concise, but the structure is raw and resembles a docstring fragment. The URL and return type are relevant, but the overall formatting is not polished. It earns a mid score because it is not verbose but also not well-structured.

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 tool with no parameters and no output schema, the description should clearly explain what data is returned, how current it is, and how it relates to other tools. The description merely states 'ETF基金份额数据' (ETF fund shares data) and the DataFrame return type. It does not mention the time span, columns, or whether it is a snapshot or time series. Also, it fails to differentiate from similar SZSE scale tools like fund_scale_daily_szse, making it incomplete for an agent to select correctly.

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, so the baseline is 4. The description doesn't need to explain parameters, and the schema confirms no required or optional inputs. The description appropriately omits parameter details since there are none.

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 source and data type: '深圳证券交易所-基金产品-基金列表-ETF基金份额' (SZSE fund products - fund list - ETF fund shares) and mentions the return type as pandas.DataFrame. While it lacks an explicit verb like 'list' or 'fetch', the resource is clearly specified and the name itself is descriptive. It is distinguishable from the sibling fund_etf_scale_sse by the 'szse' qualifier.

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 provides no guidance on when to use this tool versus alternatives such as fund_etf_scale_sse, fund_scale_daily_szse, or other ETF fund tools. There is no mention of specific use cases, prerequisites, or exclusions. Users must infer from the name and title alone, which is insufficient for tool selection.

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