diagnose_dataflow

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and the description reinforces with '[READ][risk=low]'. It goes far beyond annotations by detailing the probe sequence (connect → read(ref) → freshness → variance) and disclosing that it returns per-hop detail and a recommended action. This gives the agent a clear mental model of what happens during invocation.

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?

Although the description is longer than typical, it is tightly structured with a clear READ/risk header, a concise behavioral summary, a bulleted Args list, a Returns dict format, and an example. Every sentence adds information—there is no filler or redundancy. The front-loading of the core purpose and verdict taxonomy makes it scannable.

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 tool has no output schema, so the description must explain the return contract—and it does, listing the exact verdict strings and the structure of hops. It also covers edge cases (omitting ref, injecting series for flatline analysis) and provides a concrete example. For a moderately complex diagnostic tool, this is fully self-contained.

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%, so the description fully carries the parameter documentation load. Each of the five parameters is explained with type, purpose, and default behavior (e.g., 'ref: Tag/node/address/device to read... Omit to test connectivity only', 'freshness_threshold_s: Max value-age (seconds) before stale'). This is exemplary compensation for the empty schema 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 opens with a specific verb and resource: 'Localize a 'no data' break across an endpoint's reachable hops.' It clearly distinguishes this tool from siblings by naming it the '#1 OT triage' and enumerating distinct failure-mode verdicts (cannot_connect, comms_ok_value_stale, comms_ok_flatline), which differentiates it from generic health or anomaly tools.

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 gives clear context: use when there is a 'no data' break, and explains that it distinguishes network/PLC down, stale values, and flatline. It also provides an actionable usage hint ('Omit ref to test connectivity only'). It does not explicitly name alternative tools or state when not to use it, so it falls just short of a 5.

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