query_metrics

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

With no annotations, the description carries the full burden. It adds useful context by noting the return is time-series data aggregated over the period. However, it omits behavioral details such as pagination, error conditions, rate limits, or how aggregation is computed, leaving significant transparency gaps.

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, front-loaded sentence that immediately communicates the action and output. Every word earns its place with no redundancy or filler.

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 well-documented schema and the tool's straightforward nature, the description is sufficient for basic invocation. It mentions the return type, which is helpful, though without an output schema, a bit more detail on the response format would make it fully complete.

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 provides 100% coverage with detailed descriptions for all four parameters. The description's reference to 'specified time range' adds no additional semantic value beyond what the schema already specifies, so the baseline is appropriate.

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 clearly states it queries Datadog metrics data for a specified time range, with a specific verb and resource. It distinguishes from sibling tools like query_traces (traces) and get_metric_metadata (metadata), leaving no ambiguity about the tool's function.

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 implies the usage scenario (querying metrics over a time range) but does not explicitly mention when to prefer this over alternatives like list_metrics or get_metric_metadata. No exclusions or prerequisites are stated, so guidance is only implicit.

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