inference_analyze_inference_usage

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

Annotations are absent, so the description carries the full burden of behavioral disclosure. It states it 'analyzes' and 'provides recommendations,' implying a read-only operation, but it never explicitly says it does not modify anything. It also omits any mention of permissions, rate limits, or error conditions. For an analysis tool with no annotations, this is a notable gap.

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 and well-structured, leading with the core purpose, then clearly separated Args and Returns sections using headers. It avoids unnecessary filler and front-loads the primary intent. Slightly longer than strictly necessary but efficient.

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?

The presence of an output schema means return values are externally documented, so the description doesn't need to enumerate them, though it does list them anyway. However, it doesn't distinguish this tool from similar inference analysis tools (e.g., inference_monitor_inference_performance, inference_optimize_inference_costs) or state any prerequisites or typical use cases. Given the large sibling set, an agent might struggle to know when to select this 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?

With 0% schema description coverage, the description compensates by clarifying that subscription_id can be either an ID or a label, which adds meaningful context beyond the bare 'string' type in the schema. It doesn't specify how to obtain a label or expected format, but for a single parameter it provides useful clarification.

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 a specific action ('Analyze usage patterns') and the resource ('inference usage') with an explicit outcome ('provide optimization recommendations'). It doesn't explicitly differentiate from sibling tools like inference_monitor_inference_performance or inference_optimize_inference_costs, but the intended purpose is unambiguous.

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 is given on when to use this tool versus alternatives. The description does not mention that this tool is for analysis/recommendations while others (e.g., inference_get_inference_usage) might be for raw data retrieval, or that optimization tasks belong to inference_optimize_inference_costs. The agent is left to infer usage context, which is missing.

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