get_metric_metadata

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

With no annotations, the description carries the full burden for behavioral disclosure. It does disclose that it runs a short query and extracts sample values rather than exhaustive values, which is helpful. However, it does not explicitly state read-only behavior, potential costs, or behavior when the metric is missing, leaving some 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 two sentences and front-loaded with the main purpose. Every sentence earns its place: the first defines the outcome, the second clarifies the mechanism and the scope of values returned. No redundant phrases 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?

The tool is relatively simple, the output schema exists, and all parameters are documented in the schema. The description adds the key behavioral context: it runs a short query and returns dimension keys plus sample values. Minor gaps remain around from_time implications and result limits, but these are partially covered by the schema.

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 covers 100% of the parameters: metric_name has an example and description, and from_time has a default and description. The tool description adds no parameter-specific meaning beyond the schema, so the baseline of 3 applies.

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 starts with a specific verb and resource: 'Get metadata (dimensions and their values) for a metric.' It further explains that it runs a short query and extracts dimension keys and sample values, which clearly distinguishes this from sibling tools like query_metrics or list_metric_definitions by specifying the unique output shape.

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 intended use case is implied: use this when you need dimension keys and sample values for a named metric. However, it does not explicitly state when to prefer this over alternatives such as query_metrics or list_metric_definitions, nor does it mention exclusions or prerequisites, so the 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.