get_error_rate_analytics

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

Annotations already indicate readOnly, non-destructive, idempotent, and open-world behavior. The description adds value by disclosing the enterprise gating and resulting 403 error, and by describing output fields. No contradictions 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 extremely concise: two sentences that cover purpose, usage guidance, and important behavioral notes. Every sentence adds value with no redundancy.

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 large number of parameters (29) and the presence of a complete output schema and annotations, the description covers essential behavioral context (enterprise gating, 403 error) and provides clear purpose and alternative guidance. It is slightly less complete than ideal for complex parameter manipulation but sufficient for an analytics retrieval 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 description coverage is 100%, so the baseline is 3. The description does not add additional meaning to any parameters; it only describes the output. Thus it meets the baseline without enhancement.

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 retrieves error-rate time-series data with specific fields (summary.error_rate_percent, per-bucket percentages). It explicitly differentiates from the sibling tool get_error_analytics, which provides absolute error counts.

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 clear usage guidance: use for reliability and SLA trends, and use get_error_analytics for absolute error counts instead. It also notes the enterprise gating and 403 error for non-enterprise plans, which helps the agent decide when to invoke.

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