futures_spot_price

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, covering safety. The description adds meaningful behavioral details: it specifies the exact return object (pandas.DataFrame) and defines all output columns with types (e.g., near_basis float, date string). It also discloses default behavior when parameters are omitted. No contradictions with 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 somewhat long due to the included URL and comprehensive return schema, but the information is well-organized: purpose, parameter docs, then return fields. Each line adds value, and the front-loaded purpose makes it easy to scan. The length is justified by the need to document both parameters and output columns in the absence of schema descriptions.

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 the tool has 2 parameters, no output schema, and no nested objects, the description is remarkably complete. It explains the purpose, parameter formats and defaults, and provides a full breakdown of all return columns with types and descriptions. This fully compensates for the missing output schema and gives the agent everything needed to correctly invoke and interpret results.

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 coverage is 0%, yet the description fully documents both parameters: date accepts multiple formats (YYYY-MM-DD, YYYYMMDD, datetime.date) with a default, and vars_list is a list of contract varieties (e.g., RB, AL) with a default to all commodities. This adds substantial meaning beyond the bare schema and is essential for correct invocation.

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 provides spot prices and basis for commodities on a specified trading day. It is specific about the resource (commodity spot prices and basis) and distinguishes itself from sibling tools like futures_spot_price_previous in substance, though it does not explicitly name alternatives. A clear verb ('提供') and resource make the purpose unambiguous.

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 explains parameter defaults (empty date means today, empty vars_list means all commodities) but gives no guidance on when to choose this tool over closely related siblings such as futures_spot_price_previous or futures_spot_price_daily. There is no explicit mention of alternatives or exclusions, leaving the agent to infer usage context.

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