get_metric_data

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 output-size scaling with a formula, explains multi-series behavior when labels are omitted, and gives practical warnings for avoiding overly large responses. It does not cover auth, timeout, or error behavior, but for a read-only metric query it is fairly transparent.

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 structure is clear and purposeful: a one-line purpose, an Args section covering all parameters, and a valuable note about output size. The length is justified for a 7-parameter tool with no schema descriptions, though the Returns section adds little beyond what an output schema would already provide.

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 7 parameters, no annotations, and an output schema present, the description covers the essential call semantics, data-volume behavior, and return type. Remaining gaps are minor: behavior when start_time/end_time are omitted, and the exact effect of summary_only on the returned structure.

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 0%, but the description compensates completely by explaining every parameter with examples, units, defaults, and format notes. It also clarifies labels and summary_only semantics beyond the schema, and the volume formula gives practical meaning to start_time, end_time, and period.

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 opens with '获取 ZStack 监控数据' (get ZStack monitoring data), naming a clear verb and resource. It does not explicitly contrast with siblings like get_metric_summary or search_metric, so differentiation is inferred from names rather than stated.

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?

No guidance is given on when to use this tool versus alternatives such as get_metric_summary or search_metric. The description focuses on how to use parameters and warns about output size, but it never states when this tool is the right choice or when to prefer a sibling.

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