jev_classify_findings

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

With no annotations provided, the description carries the full burden. It discloses that the tool is 'cheap', returns 'likelihood signals only', and explicitly states it does not confirm existence of vulnerabilities. This provides meaningful behavioral insight. However, it doesn't disclose potential side effects, rate limits, or specifics about the output format beyond likelihood signals. The return type is disclosed as 'likelihood signals only', which is a positive disclosure.

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 efficient: two sentences. It conveys purpose, use context, and an explicit limitation. Every word earns its place, and the key constraint ('likelihood signals only') is front-loaded near the end of the first sentence. This is textbook conciseness.

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 tool has an output schema (though not provided in detail here), 5 parameters of which only 2 are required, and a clear differentiation from siblings. The description covers the core purpose, the triage context, and the critical limitation. It doesn't detail the 'likelihood signals' format or what the output schema looks like, but that is likely covered by the output schema itself. This is complete enough for an agent to decide when to invoke.

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?

The schema coverage is 0% in terms of parameter descriptions; all parameter semantics come from the schema structure alone. The description does not enumerate or explain individual parameters. However, the description gives context about 'triage' and 'normalization' which helps interpret the role of findings and task_goal. Given the 0% coverage, the description could do more to explain parameters, but the schema itself has reasonably descriptive names (finding, task_goal, requirements). Baseline 3 is appropriate.

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's purpose with a specific verb ('classification'), resource ('review findings'), and context (cheap normalization from multiple sources). It also distinguishes itself from siblings by mentioning 'triage' and 'likelihood signals only'. The description differentiates from sibling tools by framing this as a cheap pre-filter before expensive reasoning, which is unique among the listed siblings.

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 explicitly states when to use it ('to triage a large finding set before expensive reasoning') and what it does not do ('Does not confirm that a vulnerability or defect exists'). It implies when not to use it (when confirmation is needed), though it doesn't name specific alternative sibling tools. The usage context is clear and actionable.

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