option_margin

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds useful context: it specifies the return type (pandas.DataFrame) and the required symbol format (e.g., '原油期权'). It does not mention potential errors, data scope (e.g., per-contract vs. per-variety margin), or data source, leaving some behavioral gaps.

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 well-structured as a compact docstring: it front-loads the purpose in the first line, then clearly documents the parameter and return value. Every sentence adds value—no redundancy or filler—and the format is easy to scan.

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, read-only tool with a single optional parameter, the description covers purpose, parameter semantics, and return type. It also directs users to a sibling tool for the symbol list. It could be more complete by detailing the output DataFrame columns or clarifying what 'margin' includes (initial, maintenance, etc.), but it is largely sufficient given the tool's simplicity and strong annotations.

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 provides only the parameter name and type without any description. The description fully compensates by explaining that 'symbol' is a commodity option variety name, giving an example ('原油期权'), and referencing the helper function ak.option_margin_symbol() to obtain all valid codes and names. This is thorough and exceeds schema coverage.

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 begins with '获取商品期权保证金' (Get commodity option margin), which is a clear verb+resource statement. It identifies the tool's purpose and distinguishes it from sibling tools like option_margin_symbol (which retrieves symbols) by focusing on margin data. However, it doesn't explicitly differentiate from other option data tools such as option_premium_analysis_em, so it falls short of a full 5.

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 when to use the tool (when you need commodity option margin) and provides a helpful pointer to ak.option_margin_symbol() for obtaining valid symbol names. It does not, however, state when not to use this tool or explicitly name alternative tools, so usage guidance remains largely implicit.

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