extract_inference_result

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

Annotations are all false, so the description must carry the behavioral disclosure burden. It does not mention side effects, permissions, output format, or whether the operation is read-only. 'Extract' suggests a read operation, but readOnlyHint=false leaves ambiguity. No additional behavioral traits are disclosed.

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, front-loaded sentence with no wasted words. It clearly states the verb and object, making it highly concise and easy 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?

Given the presence of many sibling extract tools and an output schema, the description is too minimal. It does not explain when to choose this tool, the significance of the provider enum (including sglang_bench), or any prerequisites like run_id validity. It only covers the core purpose, leaving significant gaps for an agent deciding among alternatives.

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 for parameter meaning. It indirectly hints at provider values ('AIPerf', 'vLLM') but omits 'sglang_bench' and says nothing about run_id. The agent must infer parameter semantics from names and enum values alone.

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 the verb 'extract' with a specific resource: 'prompt-free AIPerf requests or vLLM aggregate measurements.' This distinguishes it from sibling tool extract_inference_trace, though it does not explicitly name any alternatives. The resource is somewhat domain-specific but clear enough for an inference-benchmarking context.

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 no explicit guidance on when to use this tool versus the many sibling extract_* tools. It implies usage when needing inference measurements, but provides no exclusions or alternative suggestions, leaving the agent without clear selection criteria.

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