Compare two passages for factual agreement

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

With no annotations provided, the description takes on full responsibility for behavioral disclosure, and it does so very well. It reveals the output shape (probability distribution, confidence, auto-versus-review), the per-aspect behavior, and the critical epistemic caveat that same_fact means mutual agreement, not truth. This is substantive behavioral context beyond the schema.

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?

Four sentences with no filler. The core verdict behavior is front-loaded, followed by optional aspects, use cases, and a caveat. Every sentence earns its place and the description is dense without being bloated.

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 moderate complexity, no output schema, and no annotations, yet the description covers the return format, optional behavior, and the meaning of a verdict. An agent has enough context to select and call this tool correctly; the schema covers the remaining parameter details.

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 coverage is 100%, so the schema already documents every parameter. The description adds genuine meaning beyond it by explaining that aspects receive independent per-aspect judgments in the same single request and that no external evidence is used, which clarifies the semantics of the purpose and aspects parameters.

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 opens with a specific verb and resource ('Judge the relation between two passages') and names the three possible verdicts, making the operation concrete. It stops short of explicitly differentiating itself from sibling tools like jev_verify or jev_classify, though the relation-judging focus is reasonably distinct within the suite.

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 explicit use cases: source reconciliation, changelog-vs-code drift, and merge sanity checks. It does not state when not to use the tool or name alternatives, but the provided contexts are clear enough for an agent to route appropriately.

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