get_futures_daily

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

Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds meaningful behavioral context: default dates to today, supported date formats, the market list with defaults, and return type as pandas.DataFrame. This goes beyond annotations, though it does not discuss edge cases like empty results or data source specifics.

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 structured docstring with a one-line summary followed by parameter documentation. It is front-loaded and free of fluff, though the return description repeats the summary phrase. Overall, it earns its place without being verbose.

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 output schema, the description lacks details about the return DataFrame's columns or contents, which would help the agent anticipate what data arrives. It does cover parameters and defaults well, but without usage guidance or return structure, it leaves notable gaps. A mention of typical columns or data coverage would make it more 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?

Schema description coverage is 0%, so the description carries full responsibility. It provides detailed docstrings for all three parameters: accepted date formats (YYYY-MM-DD, YYYYMMDD, or datetime.date), default behavior when empty, and an explicit list of market codes with their meanings. This fully compensates for the schema's lack of descriptions.

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 '交易所日交易数据' (exchange daily trading data) and documents parameters for fetching daily data. It clearly conveys the resource and action, though it uses a noun phrase rather than a verb. It implicitly distinguishes from exchange-specific siblings like get_cffex_daily by supporting a market parameter, but does not explicitly name alternatives.

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 explicit guidance on when to use this tool versus alternatives such as get_cffex_daily or get_shfe_daily. The description only explains parameters and defaults, leaving the agent to infer that this tool covers multiple exchanges. No exclusions or context for selection are provided.

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