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

With no annotations, the description carries the full burden and does so well: it discloses that the tool rules out causes with reasons, includes fix steps in its answer, and explicitly refuses to guess when observations are insufficient. These are meaningful behavioral expectations beyond the title and schema.

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 with no filler: the first gives the core behavior, the second gives usage timing and output contents, the third gives the honesty fallback. Everything earns its place and the key 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 3-parameter diagnostic with no output schema, the description tells the agent what to provide, when to call it, what the answer will contain, and how it handles ambiguous evidence. No critical gap remains for correct invocation.

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%, so the baseline is 3. The description adds value by framing observations as discriminator-check output and explaining how the tool uses them to separate causes, including the ambiguous-evidence fallback. This complements the schema without repeating it.

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 names a specific verb ('identify') and a specific resource (which root cause is present vs. ruled out, using observations from a strong knowbase_lookup match). It clearly distinguishes this from knowbase_lookup, which returns a match, and from later fix/report tools by focusing on narrowing causes.

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?

It explicitly instructs to call only after running the checks and calls itself the only way to tell several plausible causes apart. It also states the fallback when observations don't separate causes. It does not explicitly list alternative sibling tools or say 'do not use before X,' but the sequencing and exclusivity are clear.

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