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TypeScript
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Jev: noul question

jev_noul

Ask one Noul probability question about a text or JSON state and get the calibrated probability that the proposition is true as the answer.

Instructions

Ask Jev a single Noul question: estimate the probability (0..1) that a proposition about the state is true. Returns the calibrated probability under the question name "answer".

Input Schema

NameRequiredDescriptionDefault
modelNoOptional Jev model id override (default: jev-latest).
stateYesThe situation to decide about: text, a JSON object, or a JSON array.
assetsNoImages keyed by reference string. Write each reference in the state, instructions, or criteria where the image belongs. PNG or JPEG only, base64-encoded with no data: prefix. API limits: 16 MiB per image, 280-1568 px per side.
criteriaNoOptional descriptions keyed by "true" / "false" explaining what each outcome means. Values may be strings, structured objects/arrays, or null, and may hold image references.
instructionsYesThe proposition Jev should assess: plain-language text, or structured JSON.
long_contextNoOpt into Jev's 262k long-context limits (an experimental danger-zone feature: slower and less accurate).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed7 schema fields changedv0.2.3
    • removedInput schema / additionalProperties
      Removed value: -false
    • removedInput schema / properties / assets / additionalProperties / additionalProperties
      Removed value: -false
    • addedInput schema / properties / assets / propertyNames
      Added value: +{
+  "type": "string"
+}
    • changedInput schema / properties / criteria / additionalProperties / anyOf
      Previous value: -[
-  {
-    "type": "string"
-  },
-  {
-    "additionalProperties": {},
-    "type": "object"
-  },
-  {
-    "items": {},
-    "type": "array"
-  },
-  {
-    "type": "null"
-  }
-]New value: +[
+  {
+    "type": "string"
+  },
+  {
+    "additionalProperties": {},
+    "propertyNames": {
+      "type": "string"
+    },
+    "type": "object"
+  },
+  {
+    "items": {},
+    "type": "array"
+  },
+  {
+    "type": "null"
+  }
+]
    • addedInput schema / properties / criteria / propertyNames
      Added value: +{
+  "type": "string"
+}
    • changedInput schema / properties / instructions / anyOf
      Previous value: -[
-  {
-    "minLength": 1,
-    "type": "string"
-  },
-  {
-    "additionalProperties": {},
-    "type": "object"
-  },
-  {
-    "items": {},
-    "type": "array"
-  },
-  {
-    "type": "null"
-  }
-]New value: +[
+  {
+    "minLength": 1,
+    "type": "string"
+  },
+  {
+    "additionalProperties": {},
+    "propertyNames": {
+      "type": "string"
+    },
+    "type": "object"
+  },
+  {
+    "items": {},
+    "type": "array"
+  },
+  {
+    "type": "null"
+  }
+]
    • changedInput schema / properties / state / anyOf
      Previous value: -[
-  {
-    "type": "string"
-  },
-  {
-    "additionalProperties": {},
-    "type": "object"
-  },
-  {
-    "items": {},
-    "type": "array"
-  }
-]New value: +[
+  {
+    "type": "string"
+  },
+  {
+    "additionalProperties": {},
+    "propertyNames": {
+      "type": "string"
+    },
+    "type": "object"
+  },
+  {
+    "items": {},
+    "type": "array"
+  }
+]
  2. First observedv0.2.2

TDQS

A3.6/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden alone. It usefully discloses the return shape (a single calibrated probability under the key 'answer'), which is real behavioral value. However it says nothing about cost, latency, failure modes, or how the optional criteria/assets/long_context alter the estimate — notable gaps for an un-annotated inference call.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with the verb and resource, then the return contract. Nothing wraps around or repeats the name; every clause earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

It covers the core call and its return value adequately for a tool with no output schema, but with six parameters including nested objects (assets, criteria) and no annotations at all, the description leaves the optional parameters' effect on the estimate unexplained and gives no guidance on choosing among the several jev_* siblings.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline would be 3, and the description genuinely adds semantics by linking the two required parameters: the proposition in 'instructions' is evaluated against 'state'. That relational meaning is not spelled out in the schema descriptions, which treat each parameter separately.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource — 'ask Jev a single Noul question' — and pins down the operation as estimating a probability in [0,1] that a proposition about the state is true. It also names the return key, so the agent knows exactly what comes back. It stops short of naming how it differs from siblings like jev_decide or jev_score, which is why it isn't a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is only implied: the binary true/false probability framing suggests when this fits, but there is no explicit 'use this instead of jev_choice/jev_score when...' statement and no prerequisites or exclusion cases. An agent must infer the boundary from the sibling names alone.

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

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