Read anomaly detection output

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly indicates this is a read operation, scoped to nodes where an anomaly output is published, and enumerates the contents of each reading (predicted value, actual value, error, irradiance, ANOMALY/NOMINAL status). It does not state what happens when no output is published or whether results are ordered, which are minor gaps for a read-only tool.

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?

Three sentences, each adding value: the first defines the operation and scope, the second specifies the returned fields, and the third gives usage context plus a concrete example. There is no padding, and the essential information is front-loaded.

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 a simple three-parameter read tool with no output schema, the description adequately covers the return contents and gives a sibling reference for cross-checking. It could additionally state the behavior when no anomaly output is published, but overall the description is sufficient for an agent to call the tool correctly with the schema's help.

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 coverage is 100%, with descriptions for nodeId, startDate, and endDate already present. The tool description adds context about the readings but does not enhance parameter-level meaning beyond the schema, such as date range interpretation or the effect of omitting startDate. Baseline 3 is appropriate because the schema already documents the parameters.

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 a specific verb and resource: "Read the output of the platform's own ML anomaly detector for a node." It clearly distinguishes this from siblings by emphasizing the platform's own ML verdict, and later contrasts it with computing one's own verdict and with diagnose_site. An agent can tell exactly what this tool does and what it does not do.

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 explicitly states when to use the tool: "Use this when you want the platform's own verdict rather than computing one yourself, and to cross-check what diagnose_site concluded." It names the alternative (diagnose_site) and provides a decision rule, plus a concrete public example node for testing. This is strong routing guidance.

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