fund_balance_position_lg

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds that it returns a pandas DataFrame and provides the source URL, but does not disclose data volume, columns, time range, or other behavioral aspects. Minimal added context beyond 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 extremely concise: two lines containing the Chinese name, a URL, and return type information. Every sentence carries essential information with no wasted words.

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 simple, no-parameter, read-only tool with strong annotations, the description is complete. It states the data source, return type, and what the data represents. The lack of an output schema is mitigated by the clear return type declaration (pandas DataFrame).

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 an empty object, so there is nothing to explain. The baseline for 0 params is 4, and the description does not need to compensate for missing 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 clearly states the tool returns '平衡混合型基金仓位' (balanced hybrid fund position) as a pandas DataFrame, which is a specific verb+resource+scope. This distinguishes it from sibling tools like fund_stock_position_lg and fund_linghuo_position_lg which target different fund categories.

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 by stating the exact fund category (balanced hybrid), but provides no explicit when-to-use or when-not-to-use guidance, nor does it mention alternative tools for other fund types. The use case is inferred from the noun phrase, not explicitly stated.

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