Verify a yes/no assertion by providing context and a question, returning a calibrated probability from 0.0 to 1.0 for informed decision-making.
Calibrated boolean verification (Noul) returning true probability between 0.0 and 1.0.
| Name | Required | Description | Default |
|---|---|---|---|
| state | Yes | Context or assertion to verify | |
| question | Yes | Yes/No verification question |
Changes observed during successful MCP inspections.
v1.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the burden of behavioral disclosure. It discloses the output type and that results are 'calibrated,' but it does not explain side effects, interpretation of the probability, or what 'Noul' means. This is adequate for a simple verification tool but not deeply transparent.
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?
A single, efficient 14-word sentence with the core behavior front-loaded. No wasted words, though the unexplained 'Noul' is a minor clarity cost.
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?
Given low complexity (2 string params, no output schema), the description covers the output range but omits usage guidance and interpretation of the returned probability, leaving some ambiguity for an agent choosing between sibling decision tools.
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%: both 'state' and 'question' have clear descriptions. The tool description adds no parameter-level detail, so the baseline of 3 applies.
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 states a specific operation: 'boolean verification' that returns a probability between 0.0 and 1.0. It distinguishes from siblings like jev_decide_choice and jev_decide_score by emphasizing the boolean/probability output, though the term 'Noul' is unexplained.
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 phrase 'boolean verification' and the schema's 'Yes/No verification question' imply use for yes/no checks, but the description does not explicitly state when to prefer this over siblings like jev_decide_choice or jev_decide_score, nor does it mention exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.