index_global_name_table

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

Annotations already provide strong safety signals (readOnlyHint=true, destructiveHint=false, idempotentHint=true). The description adds the source URL and return type, but no additional behavioral context such as staleness of the mapping, caching, or potential differences from other mapping tools. It does not contradict the annotations, but it adds minimal value beyond them.

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, containing only the title, a reference URL, and the return annotation (:return: and :rtype:). Every line provides useful information without waste. The structure is clean and front-loaded with the core purpose.

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 zero-parameter tool with no output schema, the description is minimal but somewhat adequate—it tells the agent that the tool returns a DataFrame mapping names to codes for global indices. However, it does not specify the exact columns or how the mapping may be structured, and it lacks any contextual notes on when this mapping would be needed or how it differs from other mapping tables in the sibling set. It is complete enough to invoke correctly but leaves room for uncertainty about the return format.

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 input schema is empty (0 parameters), so there are no parameter semantics to explain. The baseline for 0 parameters is 4, and the description does not need to compensate. It also does not introduce any confusion about parameters.

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 states it is a '名称代码映射表' (name/code mapping table) for Sina Finance Global Markets, and explicitly notes the return type is a pandas DataFrame with the URL as a reference. This clearly identifies the resource and function, though the verb is implicit rather than explicit. It distinguishes from sibling index tools that provide historical or spot data by being a mapping table.

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?

No usage guidance is provided. The description does not state when to use this tool versus alternatives like index_global_hist_em or index_global_spot_em, nor any prerequisites or exclusions. It only describes what the tool returns, leaving the agent without context for selection.

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