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

No annotations are provided, so the description carries the full behavioral burden. It discloses paid tier requirement (Standard ¥29.9+，Trial不可用), independent daily quotas (Standard 200 / Pro 500 / Unlimited 2000), unsupported-instrument error behavior, and a reproducibility caveat for backtesting (qfq序列会重算).

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?

Front-loaded purpose followed by structured caveats. Dense but nearly every sentence is decision-relevant for this complex, paid, sibling-overlapping tool. Formatting (bold, ⚠️) aids scannability, though it is on the verbose side.

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?

An output schema exists, so return columns needn't be repeated, yet the description still lists them. Combined with instrument scope, error behavior, auth/quota details, and fq semantics, an agent has everything needed to call it correctly.

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 100%, so baseline is 3. The description adds meaningful parameter semantics beyond the schema: fq choice guidance for backtesting (hfq/nofq vs default qfq) and the start default (2016-08-08). Some content overlaps the schema, keeping it from a 5.

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?

States a specific resource and scope: '全历史K线（10 年日线）+ 可选复权口径'. Explicitly differentiates from sibling ashare_kline with three concrete dimensions (data volume, fq parameter, instrument scope), so an agent can select correctly without opening either schema.

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?

Provides explicit trigger phrases ('十年走势/长期回测/全历史数据/复权价/后复权/原始价') and clear exclusions (ETF/可转债/指数/港美股等会报错，请改用 ashare_kline). Also gives conditional advice: '做回测请用 fq=hfq 或 fq=nofq'.

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