get-metric-metadata

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

With no annotations provided, the description carries the burden of behavioral disclosure. It states that the tool returns type, unit, and description, which implies a read operation. However, it does not address potential side effects, auth requirements, or data completeness (e.g., metadata might be absent for some metrics). The description is minimally adequate.

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, highly efficient sentence. It front-loads the key purpose and includes the list of returned fields. Every word contributes meaning, with no redundancy.

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 simplicity (one parameter, read-only, no output schema), the description is nearly complete. It specifies the input and output fields. However, it could mention that metadata might not exist for all metrics or that the fields could be null, which would improve completeness.

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 covers 100% of parameters with a clear description of metricName. The description adds no additional semantic value beyond the schema; it merely restates the purpose. Given high schema coverage, a baseline score of 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 gets 'metadata for a specific Datadog metric' and enumerates the returned fields (type, unit, description). This verb+resource definition is specific and distinguishes it from sibling tools like get-metrics (which likely returns metric names) and list-active-metrics.

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 does not provide explicit guidance on when to use this tool versus alternatives such as get-metrics or list-active-metrics. However, the purpose is straightforward enough that an AI agent can infer its use for metadata lookup, but it lacks explicit usage context.

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