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subq-snap-mcp

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TypeScript
Remote

Snap: noul question (yes/no probability)

snap_noul

Assess whether a proposition about a state is true and return a calibrated probability (0–1) for routing, triage, moderation, or risk-scoring workflows.

Instructions

Ask SubQ's Snap decision model a single Noul question: is a proposition about a state true? Returns a calibrated probability (0..1) under the question name "answer".

Input Schema

NameRequiredDescriptionDefault
modelNoOptional Snap model id override (default: subq-snap-1.1).
stateYesThe situation to assess: text, a JSON object, or a JSON array.
imagesNoUp to 4 images for this request. PNG, JPEG, or WebP, base64-encoded with no data: prefix; at most 4 MiB decoded each, 8 MiB in total. Every question in the request sees every image.
criteriaNoOptional descriptions keyed by "true" / "false" explaining what each outcome means. Values may be strings, structured objects/arrays, or null.
instructionsYesThe proposition Snap should assess: plain-language text, or structured JSON.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed6 schema fields changedv0.1.2
    • removedInput schema / additionalProperties
      Removed value: -false
    • 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"
+}
    • removedInput schema / properties / images / items / additionalProperties
      Removed value: -false
    • 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 observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

No annotations, so the description carries the burden, and it does disclose the return shape: a calibrated probability in 0..1 keyed under "answer" — valuable since there is no output schema. It says nothing about model defaults, cost, latency, error behavior, or whether batching is possible.

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 action, then the return contract. No filler and nothing repeated from structured fields.

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

Completeness4/5

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

Given five parameters, nested object/array inputs, and no output schema, the description supplies the missing return-value contract and the yes/no scope. It leaves out only secondary behavioral context such as default model behavior and image handling, which the schema mostly covers.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents every parameter including model override, images limits, and criteria keys. The description's phrase 'a proposition about a state' adds only a light conceptual gloss and no format or constraint detail beyond the schema.

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 the Snap decision model a single yes/no Noul question about whether a proposition on a state is true. The word 'single' implicitly separates it from sibling multi-question tools like snap_choice, but no sibling is named explicitly.

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 by 'single Noul question' and the yes/no framing. With four siblings (snap_decide, snap_choice, snap_score, snap_list_models) in the catalog, an explicit 'use snap_choice for multi-option, snap_score for grading' statement is missing.

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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