获取期现价差

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

No annotations are provided, so the description must carry the transparency burden. It only states the tool 'gets' data, implying a read-only operation, but it offers no additional behavioral context such as data coverage (e.g., historical vs current), update frequency, or any limitations. This is minimal disclosure beyond the tool's name.

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 single focused sentence with clear structure: verb, resource, and purpose. No redundant 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?

Given there is no output schema and no annotations, the description should explain what the returned basis data looks like (e.g., timeframe, unit, series) and when to use it. It lacks these details, leaving the agent with only the assurance that some basis data is returned. Completeness is therefore limited.

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 fully documents the single 'symbol' parameter with a default value and a comprehensive list of supported varieties. The description adds no parameter-specific detail, so it adds no value beyond the schema; baseline of 3 applies.

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 it retrieves basis data between futures and spot prices, with the purpose of assessing market expectations and arbitrage opportunities. This distinguishes it from sibling tools like futures_prices which handle absolute prices.

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 for basis analysis (market expectations, arbitrage) but gives no explicit when/when-not guidance or references to alternative tools. The intended context is clear but not formally differentiated from siblings.

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