get_gfex_daily

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

Annotations already indicate read-only, non-destructive, idempotent behavior. The description adds context about the default date (current trading day), accepted date formats, and the return type (pandas DataFrame), which are useful. It does not mention edge cases like non-trading-day inputs or data availability limitations, but overall it supplements 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 a well-organized docstring with summary, product info, source URL, and parameter/return documentation. Each line provides distinct value, though the return and rtype lines somewhat repeat the opening summary. It is concise for the information it conveys.

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 one-parameter read-only tool without an output schema, the description covers the essential context: source URL, product, date semantics, and return type. It could be improved by listing DataFrame columns or explicitly stating the scope is limited to the industrial silicon contract, but given the simplicity and annotations, it is largely complete.

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 schema has one parameter with no description coverage. The description thoroughly documents the 'date' parameter, specifying supported formats (YYYY-MM-DD, YYYYMMDD, datetime.date), the default behavior, and its type. This fully compensates for the schema's lack of detail.

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 retrieves Guangzhou Futures Exchange daily volume-price data (广州期货交易所-日频率-量价数据) and specifies the product (industrial silicon, listing date 20221222). This identifies the exact resource and differentiates it from sibling exchange-specific tools like get_cffex_daily or get_dce_daily.

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 when GFEX daily data is needed, and the name and product detail make the exchange clear. However, it does not explicitly name alternative tools for other exchanges nor state when not to use this tool, so guidance is only implied.

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