pred_gmv

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

No annotations are provided, so the description carries the full burden. It states the tool performs prediction, implying it's a read-only operation that doesn't modify data, but it doesn't disclose behavioral traits such as accuracy, assumptions, data sources, error handling, or output format. The description is minimal and lacks essential context for safe and effective use.

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 very concise with two short sentences, front-loading the purpose ('Alink教程第1章5.4节的Pipeline预测') and usage ('输入年份，预测GMV'). There's no wasted text, but it might be overly terse, lacking necessary details. Every sentence contributes, but more could be added for clarity.

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 no annotations, no output schema, and low schema coverage, the description is incomplete. It doesn't explain what GMV is, the prediction model's basis, return values, or error cases. For a prediction tool with one parameter, this leaves significant gaps in understanding how to use it effectively.

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 has 1 parameter with 0% description coverage, so the description must compensate. It mentions '输入年份' (input year), implying the parameter 'x' represents a year, but it doesn't specify format (e.g., integer year like 2023), range, or units. This adds some meaning beyond the bare schema but is insufficient for clear usage.

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 the tool performs 'Pipeline预测' (pipeline prediction) for GMV given a year input, which provides a general purpose. However, it's vague about what 'GMV' specifically refers to (e.g., Gross Merchandise Value) and lacks differentiation from the sibling tool 'calc', which might also perform calculations. It's not tautological but lacks specificity.

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 '输入年份，预测GMV' (input year, predict GMV), implying usage for year-based GMV predictions. However, it provides no explicit guidance on when to use this tool versus the sibling 'calc' tool, no prerequisites, and no alternatives or exclusions. Usage is implied but not clearly defined.

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