Analyse metric data

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

Annotations provide readOnlyHint: true, and the description's actions ('evaluate', 'surface anomalies') are consistent with a read-only analysis. The description adds behavioral details like returning recommendations and next steps, which goes beyond the annotation alone.

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, concise sentence that front-loads the funnel step and clearly conveys all necessary information without any extraneous words.

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 presence of an output schema, the description adequately explains the tool's role, inputs, and outputs. It provides step context and mentions the return of recommendations and next steps, making it effectively complete for an analysis tool.

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 100%, but the description adds context by noting that 'data_metrics' is the output of 'get_data_metrics'. This helps the agent understand the expected input format beyond the schema's generic description.

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 uses specific verbs ('evaluate', 'surface', 'return') and identifies the resource ('metric data'). It explicitly labels itself as 'Step 4 of the funnel', distinguishing it clearly from siblings like 'diagnostic_categorize', 'find_metrics', and 'get_data_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 positions the tool as part of a sequential funnel ('Step 4'), implying it should be used after prior steps. However, it does not explicitly state when not to use it or what alternatives exist beyond the sibling names.

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