Yes/no as calibrated P(yes)

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

The description adds behavioral context beyond the annotations: it explains that 'there is no separate confidence for a noul answer' and that values near 0.5 should be treated as unresolved. It also states the read-only nature explicitly, aligning with readOnlyHint. It mentions parallel evaluation of state, which is useful execution detail. This exceeds the annotation coverage and provides meaningful interpretation guidance.

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 three sentences, front-loaded with the core purpose, then adds the critical interpretation caveat, and finally the read-only note. Every sentence earns its place; there is no filler. It is concise and well-structured.

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 description explains the primary return value (P(yes)) and its interpretation, which is the most important aspect for an agent. However, with no output schema and complex options (e.g., raw, include_probabilities, include_usage), the description does not outline the full response structure beyond the probability. The schema parameter descriptions help, but a bit more detail on the default response format would improve completeness. Given the tool's complexity, this is a minor gap.

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 already has 100% description coverage for all parameters, so the baseline is 3. The tool description itself does not elaborate on parameters beyond the schema, but it does not need to—the schema covers semantics well. The description's mention of 'calibrated probability' relates to the output, not parameters, so no additional parameter semantics are added.

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 function: 'Ask Jev a yes/no question and get back P(yes) as a calibrated probability.' It also explains the key output interpretation ('the probability is the answer'), which distinguishes it from sibling tools like jev_score or jev_choice. This is a specific verb+resource statement that leaves no ambiguity about what the tool does.

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 implies when to use it ('Ask Jev a yes/no question') but does not explicitly compare it to siblings or state when NOT to use it. It does not mention alternatives like jev_score (which likely returns a score) or jev_choice (which chooses among options). The context is clear enough for an agent to infer the use case, but explicit exclusions are missing.

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