chatwoot_get_label_performance

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description's 'Returns' aligns with that. However, the description adds no additional behavioral context such as output format behavior, limitations, or data freshness. No contradiction with annotations.

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 short and front-loaded with the core purpose. The second sentence partially restates the first, but overall it is scannable and contains no unnecessary padding.

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?

For a read-only tool with one required parameter and enum-constrained optional parameters, the description plus schema is sufficient to make a correct call. It could mention default period/output_format or metric specifics, but output schema and enums cover the remaining details.

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 must compensate. It usefully explains label_id by pointing to chatwoot_list_labels, but period is only 'Analysis period' and output_format only 'Output format' – tautological and no better than the property names. It does not describe the meaning of period values or the difference between markdown and json.

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 states a specific verb and resource: 'Returns performance metrics for a specific label,' and reinforces it with 'quantitative analysis of conversations by category/tag.' This clearly distinguishes it from sibling performance tools that target agents, inboxes, or teams.

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 implies usage for label-scoped quantitative analysis, but it does not explicitly state when to prefer this over sibling performance tools or provide any exclusions. No alternatives are named, leaving the routing decision mostly to inference.

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