get_metric_history

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

With no annotations, the description must convey all behavioral traits. It only states the action (reading sample values) and does not disclose side effects, permissions, or error behavior, nor explicitly confirm it is read-only.

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 a single, clear sentence with no redundant information, making it highly concise and well-structured.

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?

The description covers the tool's purpose, inputs, and intended use case. Output format is likely handled by the output schema (which exists). It lacks edge-case details but is adequate for a simple read operation.

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 description adds meaning by indicating that '某指标' refers to the metric parameter, '设备' to device_id, and '最近 N 个采样值' to points. However, it does not explain the default value of points or any constraints, leaving some gaps given the schema lacks descriptions.

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 that the tool reads recent N sample values of a device metric, specifying the verb (read), resource (metric history), and scope (device). It differentiates from siblings by mentioning time series data and visualization use cases.

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 a use case (trend charts / 3D visualization) but does not explicitly advise when to use this tool over alternatives like get_device_metrics. The guidance is implicit rather than explicit.

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