query_metric

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

The description explains the tool builds a timeseries query and executes against the Grail storage API, disclosing the required scope. Without annotations, it carries the burden but could add more details like error handling, idempotency, or rate limits. It is adequate but not rich.

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?

Three sentences cover purpose, mechanism, and scope requirement without waste. Each sentence adds value. Slightly more structure could help, but it is already concise and clear.

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?

The tool has moderate complexity (7 params), no output schema, and no annotations. The description covers purpose and scope but does not explain the return format or pagination behavior (considering limit parameter). Adequate but incomplete.

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% with detailed descriptions for all 7 parameters. The description adds that it 'builds a timeseries query', which provides context but does not significantly enhance parameter meaning beyond the schema. Baseline 3 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 the tool queries metric data points via Grail DQL 'timeseries', specifying it is Gen3-native and uses a platform token. This distinguishes it from sibling tools like get_metric_metadata (metadata) and execute_dql (generic DQL).

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 provides clear prerequisites: works on Gen3/Grail tenants with storage:metrics:read scope. It does not explicitly mention when not to use or alternatives, but the context implies it is for data points rather than metadata or other operations.

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