cja_validate_calculated_metric

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

With no annotations, the description carries full burden. It discloses the tool is a validation step (non-destructive), and specifies return values including validation result, metrics used, and functions detected. No side effects are mentioned, but the nature implies read-only.

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 well-structured: purpose sentence, usage sentence, bulleted Args, Returns, and example queries. It is concise, front-loaded, and each part adds value.

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 5 parameters and no output schema or annotations, the description fully explains all inputs and output structure. It provides sufficient context for an agent to use the tool correctly, including example queries.

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 0%, so description fully compensates by detailing each parameter: name required, definition required, metric_type with default 'decimal', description optional, dataview_id optional with default behavior. This adds clear meaning beyond the raw schema.

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 validates a calculated metric definition before creation or update, using specific verbs ('validate', 'check') and distinguishing from siblings like create or segment validation.

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 explicitly advises 'Always use this before creating a new metric', giving clear when-to-use guidance. Although no explicit when-not-to or alternatives are mentioned, the context is strong enough.

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