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

With no annotations, the description carries the full burden of behavioral disclosure, but it only states the tool's purpose. It does not mention default window behavior, whether the response contains both aggregate and time-series data, error behavior, or any read-only guarantees beyond the verb 'Get'.

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 a single clear sentence with no filler. It front-loads the core action and resource, and every word earns its place.

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 tool with no output schema and no annotations, the description is too sparse to support fully correct invocation. Missing details include the default time window, whether snippet_id is still honored, what metric names are returned, and how aggregate and time-series outputs are structured.

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 schema already documents workflow_id and snippet_id with descriptions, so the description adds little at the parameter level. The window parameter has no schema description and the tool description does not explain how it affects aggregate versus time-series results, though its enum values are self-explanatory.

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 states a specific verb ('Get'), a clear resource ('metrics'), and the scope ('for a workflow'). It is distinct from sibling tools like get_workflow, get_logs, and get_invocation because it identifies aggregate and time-series metrics as the output.

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 wording implies use when workflow metrics are needed, but the description gives no explicit guidance on when to prefer this tool over siblings such as get_workflow or get_logs. It also does not clarify how the window parameter should be selected for aggregate versus time-series needs.

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