Get Python metric preview

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

Annotations already establish read-only, idempotent, non-destructive behavior, so the description only needs to add what they don't cover. It does: the call is asynchronous, results are not final until a terminal state, and the preview ID must belong to the exact draft. The state vocabulary itself (pending/running/succeeded/failed) is never enumerated.

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?

Two sentences, zero filler. The operation is stated first, then the polling and publish-precondition guidance in the same compact sentence.

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?

With no output schema, the description carries the burden of explaining return values, and 'state and result' is not enough for a polling tool: an agent cannot tell which state values are terminal or what a failed preview returns. Annotations and the fully described schema cover the rest adequately.

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% and both parameters are documented, so the baseline is 3. The description adds a real constraint not present in the schema: the preview ID must correspond to the exact draft, i.e., the two IDs are a matched pair.

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?

States a specific verb and resource: retrieve state and result of an asynchronous Python metric preview. The lifecycle framing (poll, then publish) implicitly separates it from create_python_metric_preview, cancel_python_metric_preview, and publish_python_metric_draft without ambiguity.

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?

Explicitly says to poll repeatedly until a terminal state is reached, and gives a gating rule for publish ('only with successful preview IDs for the exact draft'). It doesn't mention the alternative actions on failure (e.g., cancel_python_metric_preview) or when not to call, which keeps it 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.