apic_get_cpu_utilization

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

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It describes return values but does not mention that the operation is read-only (e.g., no destructive side effects), authentication requirements, rate limits, or whether data is real-time or historical.

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?

Two concise sentences with no extraneous information. Front-loaded with the primary action and followed by specific details, making it efficient for an AI agent to parse.

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 simple tool with no parameters and no output schema, the description adequately explains what the tool does and what it returns. It is missing potential details like time range or snapshot nature, but is otherwise complete for a straightforward retrieval operation.

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?

There are zero parameters, and schema description coverage is 100% (trivially). The description adds value by detailing the output fields (CPU usage statistics per node, user/kernel/idle/wait times), which goes beyond the empty 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 retrieves CPU utilization for all fabric nodes and lists specific metrics (average, max, min, user, kernel, idle, wait). The verb 'Retrieve' and resource 'CPU utilization for all fabric nodes' are unambiguous, distinguishing it from sibling tools like 'apic_get_resource_utilization'.

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?

No guidance on when to use this tool versus alternatives, such as 'apic_get_resource_utilization' which may also cover CPU. No prerequisites, exclusions, or context for invocation are provided.

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