get_metric_field_values

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves values for discovery, implying a read-only operation, but doesn't cover aspects like rate limits, authentication needs, error handling, or response format details. This is a significant gap for a tool with no annotation coverage.

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, efficient sentence that front-loads the core action and purpose without any wasted words. It directly communicates what the tool does and why, making it highly concise and well-structured.

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 no annotations, no output schema, and 100% schema coverage, the description is minimally adequate. It clarifies the tool's purpose but lacks behavioral details like response format or operational constraints. For a tool with moderate complexity (3 parameters, no output schema), it should provide more context to be 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?

Schema description coverage is 100%, so the schema fully documents all three parameters with descriptions and an enum for 'format'. The description adds no additional parameter semantics beyond what's in the schema, such as examples or constraints. Baseline 3 is appropriate when the schema handles parameter documentation effectively.

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 action ('Get all possible values') and resource ('for a specific field of a metric from Datadog'), with the purpose 'to discover available dimensions'. It distinguishes from siblings like 'get_metric_fields' (which likely lists fields rather than values) and 'list_metrics' (which lists metrics). However, it doesn't explicitly contrast with 'get_logs_field_values' for logs versus metrics, keeping it from a perfect score.

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 no guidance on when to use this tool versus alternatives. It doesn't mention siblings like 'get_metric_fields' (for listing fields) or 'get_logs_field_values' (for logs), nor does it specify prerequisites or exclusions. Usage is implied by the purpose but lacks explicit context for selection.

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