query_metrics

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

With no annotations present, the description carries the full burden and does so thoroughly. It discloses async polling behavior, the done:false protocol, the shape of returned data points, possible status values, and the required API scope. This is far beyond a minimal description and gives the agent reliable expectations for behavior.

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 compact four-sentence paragraph with no filler. It front-loads the core purpose, then covers return shape, async behavior, and authentication requirements in logical order. Every sentence contributes meaningful information.

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 the tool's complexity—9 parameters, no output schema, and no annotations—the description is remarkably complete. It explains what the response contains, how slow operations work, how to recover a pending operation, and what permission is required. There is little an agent would need to infer or discover elsewhere.

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 input schema already provides 100% parameter coverage with detailed descriptions for every field, so the baseline is 3. The description adds high-level context about bucketing, breakdowns, and filtering, but it mostly restates what the schema documents rather than adding new parameter-level semantics.

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 and resource: 'Query a time series for one analytics metric of a Roblox universe.' It lists concrete metric examples and differentiates itself from siblings like list_metrics, list_dimension_values, and get_operation by focusing on actual data retrieval and even explaining when to delegate to get_operation.

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 clearly establishes when to use the tool: any time you need metric time-series data for a universe. It also provides an explicit routing rule for slow queries ('if the response has done: false, call get_operation'), but it does not explicitly state when to prefer list_metrics or list_dimension_values over this tool, though the schema does mention them.

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