get_rank_sum

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

The annotations already mark this as readOnly, idempotent, and non-destructive, and the description adds meaningful behavioral context: note 1 explains that SHFE and CFFEX rankings are aggregated from instrument totals rather than being true variety totals, and note 2 notes that DCE does not publish instrument-level rankings. This goes beyond the structured fields and sets accurate expectations about data limitations.

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 structured with a purpose line, two important notes, then parameter/return documentation. Each section serves a clear function and the most critical caveats are front-loaded. It is longer than a typical description, but the extensive column listing is useful given there is no output schema, and nothing seems redundant.

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 read-only retrieval tool with two parameters, it explains parameter formats, return type, and key aggregation caveats. However, the return column listing is incomplete (stops at vol_top10 even though the title implies top15/top20 fields), and the five exchanges are not explicitly named. Since there is no output schema, these gaps leave some ambiguity for an agent.

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 gives no descriptions for either parameter, so the description carries the full burden. It explains acceptable date formats (YYYY-MM-DD, YYYYMMDD, datetime.date) and the empty-date behavior (defaults to today), as well as vars_list format with an example (['RB','AL']) and the empty-list behavior (all commodities). This compensates well for the 0% 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 clearly states it collects top 5/10/15/20 member position ranking data from five futures exchanges (采集五个期货交易所前5、前10、前15、前20会员持仓排名数据). The caveats (注1 and 注2) further specify what is being returned at an aggregated level. However, it does not explicitly contrast this with sibling per-exchange ranking tools, so it stops short of a perfect score.

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 provides useful context about the aggregation methodology and exchange-specific limitations, which indirectly helps an agent infer when to use this tool (e.g., when cross-exchange aggregated rankings are needed). It does not explicitly say 'use this when' or 'use alternatives instead,' so the guidance remains implied rather than explicit.

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