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

With no annotations, the description partly discloses behavior: returns raw figures + data_ref, doesn't show table, requires explicit scenarios with aliases. But it omits side effects, error handling, or permissions needed.

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 concise with two sentences, front-loading the core purpose. It is well-structured but could benefit from bullet points for parameter details.

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 complexity (13 parameters, no output schema, no annotations), the description lacks completeness. It does not explain output format, error cases, or behavior with missing optional parameters.

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 0%, so the description should explain parameters. It only mentions metrics, dimensions, scenarios, and data_ref. Many other parameters (scale, layout, filters, decimals, etc.) are left unexplained, making it hard for an agent to use correctly.

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 breaks metrics down by dimensions, with concrete examples like 'revenue by project'. It distinguishes from siblings implicitly by mentioning downstream use with eval_chart_transform, but does not explicitly differentiate from the similar run_metric sibling.

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 gives usage context: 'Does not show the user a table' and suggests passing data_ref to eval_chart_transform. However, it does not specify when to avoid using this tool or explicitly name alternative tools for different use cases.

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