jev_noul

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It reveals that the tool is 'Fast' and 'Returns calibrated truth probability and boolean verdict', which are useful behavioral traits. However, it omits details on error handling, determinism, or how contradictory criteria are resolved. The mention of 'Jev's Noul decision primitive' is opaque and not explained, leaving some ambiguity.

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 compact two-sentence structure with the core function front-loaded ('Fast boolean proposition evaluation'). Every sentence serves a purpose: the first states the action and primitive, the second states the output and use cases. No fluff or repetition.

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 four parameters, no output schema, and no annotations, the description provides a clear overview, mentions the return type, and lists typical use cases. It does not offer examples or clarify the 'Jev's Noul decision primitive' but covers the essential information an agent needs to invoke the tool correctly. The schema fills in parameter details, so the description is nearly complete for this complexity.

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 100%, so all four parameters (context, instruction, true_criteria, false_criteria) are already documented in the schema. The description does not add any additional meaning beyond what the schema provides; it focuses on the tool's purpose and output rather than parameter details. Baseline 3 is appropriate since the schema handles the heavy lifting.

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 verb and resource: 'Fast boolean proposition evaluation (True/False)'. It also lists concrete use cases (verification, assertions, bug detection, criteria checks) and the return type (calibrated truth probability and boolean verdict). This distinguishes it from siblings like jev_choice (multi-choice) and jev_score (scoring) without needing to open the schema.

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 provides clear usage context via 'Ideal for verification, assertions, bug detection, and criteria checks', indicating when to apply it. However, it does not explicitly state when NOT to use it or mention alternatives like jev_choice or jev_batch_decisions. The guidance is implied rather than explicit, but strong enough for an agent to infer appropriate scenarios.

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