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

The annotations already declare this as a read-only, idempotent, non-destructive, closed-world operation. The description adds useful behavioral detail by explaining the return shape, ordering, and a 100-website limit, plus that no customer or message data is included.

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 three tight sentences with no filler. It is front-loaded with the usage condition, then the return behavior, then the exclusion, which is an effective structure for an agent deciding whether to call it.

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?

Given that there are no parameters and an output schema exists, the description already provides more than enough context: when to use it, what it returns, ordering, a limit, and what it does not cover. Nothing needed to invoke it correctly is missing.

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 tool takes zero parameters, so there is no parameter semantics for the description to clarify. Per the rubric, a zero-parameter tool has a baseline of 4.

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 gives a specific verb and resource: it counts customer conversations that are open or unresolved, either in total or per website. It also explicitly distinguishes itself from sibling tools like list_conversations or get_conversation by saying it is not for finding which conversations are open.

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 clearly states when to use the tool: when someone asks how many customer conversations are still open or unresolved, in total or per website. It also provides a negative condition, 'Not for finding which conversations are open,' though it does not name the alternative tool to use instead.

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