extract_inference_result

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

With all annotations false, the description carries the burden of behavioral disclosure, but it only says 'Extract.' It does not clarify whether the operation is read-only, whether it writes extraction artifacts, whether it requires an existing run, or what side effects may occur. 'Extract' implies retrieval but does not make the behavioral profile explicit.

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 dense sentence with no filler and starts with the action verb. It is well-structured and scannable, though its brevity contributes to missing contextual detail.

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 tool with two required parameters, no annotation safety profile, and many sibling extractors, the description is too thin. It should clarify run_id semantics, cover all provider enum values, and distinguish itself from similar extraction tools. The output schema helps with return shape but not with selecting or invoking the tool correctly.

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 adds some semantics for provider values (aiperf maps to requests, vllm_bench maps to aggregate measurements) but says nothing about run_id and omits the sglang_bench provider entirely.

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 names a concrete action ('Extract') and a specific resource ('prompt-free AIPerf requests or vLLM aggregate measurements'), which makes the tool's core purpose identifiable. It is reasonably distinct from sibling extraction tools, though 'prompt-free' and 'inference result' are domain jargon and the sglang_bench provider is omitted.

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 alternatives such as extract_inference_trace, query_measurements, or list_inference_requests. It does not state prerequisites, exclusions, or conditions that would help an agent choose this over a sibling.

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