jev_noulEvaluate any input and get a yes/no judgment with a numeric probability, where 0 means no, 1 means yes, and values near 0.5 indicate uncertainty.
Yes/no judgment. Returns {type, noul (0=no..1=yes)}. Near 0.5 = uncertain.
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | jev-latest | |
| state | Yes | ||
| endpoint | No | direct | |
| true_means | No | ||
| false_means | No | ||
| instructions | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must fully disclose behavior. It explains the output shape and the uncertainty interpretation, but omits side effects, authentication, rate limits, and the meaning of parameters like 'state' and 'instructions'. The disclosed information is minimal.
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 concise, with the core purpose front-loaded in the first sentence and the output format in the second. It avoids redundancy, though its brevity contributes to incompleteness.
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 six parameters, two required, and zero schema descriptions or annotations, the description is grossly incomplete. It does not explain what 'state' or 'instructions' are, what true_means/false_means do, or any prerequisites. The presence of an output schema does not compensate for the missing parameter semantics.
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 0%, and the description does not explain any of the six parameters (state, instructions, model, endpoint, true_means, false_means). It adds no semantic value beyond the schema, leaving the agent to infer parameter meanings.
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 provides a yes/no judgment, with a specific return format and interpretation of the noul value. This distinguishes it from siblings like jev_choice or jev_score, though it does not name them explicitly.
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?
No guidance is given on when to use this tool versus the sibling tools (jev_evaluate, jev_choice, jev_score, jev_models). It implies a yes/no use case but does not specify exclusions or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.