indicator_wsma

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

No annotations are provided, so the description must carry full behavioral disclosure. It only notes a data point requirement and does not describe computational behavior, side effects, or output format. The agent is left without knowing what the function returns or if it has any non-obvious traits.

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 extremely concise: one sentence front-loading the indicator name and a key prerequisite. Every word serves a purpose, and there is no redundancy or filler.

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 lack of an output schema, the description should explain the return value (e.g., an array of WSMA values). It does not, making it incomplete for an agent to assess usage. Context about typical usage or relation to other moving averages is also missing.

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?

Both parameters have schema descriptions ('价格数组' and '计算周期'), providing basic semantics. The description adds the constraint that the values array must have at least 'interval' items, which reinforces the schema minima. With 100% schema coverage, the extra information is valuable but not extensive.

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 it computes Wilder's Smoothed Moving Average (WSMA), which is clear. However, it does not differentiate from sibling tools like indicator_ema, indicator_sma, or indicator_wma, leaving the agent to infer the difference from the name alone.

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 mentions the requirement of at least 'interval' data points, providing a precondition. But it offers no guidance on when to choose WSMA over other moving averages, nor any context like typical use cases or limitations.

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