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subq-snap-mcp

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Snap: choice question

snap_choice

Chooses one of 2-255 labeled options for a given state using SubQ's Snap decision model, returning the picked option, a confidence value, and a probability for every option.

Instructions

Ask SubQ's Snap decision model a single Choice question: pick one of 2-255 labelled options for a state. Returns the chosen option, a confidence value, and a probability for every option, under the question name "answer".

Input Schema

NameRequiredDescriptionDefault
modelNoOptional Snap model id override (default: subq-snap-1.1).
stateYesThe situation to decide about: 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.
optionsYesObject mapping each option label to a description (string, structured object/array, or null; 2-255 options).
instructionsYesWhat Snap should decide: 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
    • 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 / options / 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 / options / propertyNames
      Added value: +{
+  "type": "string"
+}
    • 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 are provided, so the description carries the full burden. It does usefully disclose the return shape (chosen option, confidence, per-option probabilities under the key 'answer'), but says nothing about determinism, cost, rate limits, or failure behavior for a model-inference tool.

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?

A single economical sentence with zero waste, front-loading the action and resource before the return contract. Every clause earns its place.

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?

With no output schema, the description correctly explains the return values and the answer key, and the schema fully documents the five parameters including nested image constraints. Minor gaps remain around usage differentiation and model behavior, but the essential calling information is complete.

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 state, instructions, options, images, and model. The description adds the 2-255 option-count constraint but otherwise repeats what the schema provides; baseline 3 applies.

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 Choice question and pick one of 2-255 labelled options. The word 'single' and 'Choice' implicitly distinguish it from multi-question or scoring siblings, 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 the phrase 'single Choice question'; there is no explicit when-to-use, when-not, or routing to snap_decide or snap_score. An agent must infer the choice between siblings from 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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