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subq-snap-mcp

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TypeScript
Remote

Snap: score question

snap_score

Rate a state on a custom ordered scale of 2–10 levels and return a probability-weighted score, per-level probabilities, and confidence for triage, routing, or risk scoring.

Instructions

Ask SubQ's Snap decision model a single Score question: rate a state on an ordered scale of 2-10 levels you define. Returns a probability-weighted score (it can fall between levels), the level legend, per-level probabilities, and a confidence value, under the question name "answer".

Input Schema

NameRequiredDescriptionDefault
modelNoOptional Snap model id override (default: subq-snap-1.1).
stateYesThe situation to rate: 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.
levelsYesOrdered level descriptions, lowest to highest (2-10). Each level may be a string or a structured entry.
instructionsYesWhat Snap should rate: plain-language text, or structured JSON.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed5 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 / levels / items / 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"
+  }
+]
    • 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

A3.8/5.0
Behavior4/5

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

No annotations are present, so the description must carry behavioral context. It does well by describing the returned probability-weighted score that may fall between levels, the level legend, per-level probabilities, and confidence under 'answer'; it does not cover limits, model defaults, or error behavior.

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 tight sentences with the action and return value front-loaded; no repeated or filler content.

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 usefully explains the return payload and the between-level scoring semantics. It remains complete for core invocation, with the main gap being routing guidance against sibling Snap tools.

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, levels, images and model. The description reinforces the ordered scale and rating target but adds little parameter 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?

The description states a specific verb and resource: ask the Snap model a single Score question and rate a state on a user-defined ordered scale. It clearly separates scoring from a generic decision, though it does not explicitly name sibling tools or state when to choose snap_score over snap_choice/snap_decide.

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 implied by 'single Score question' and the 2-10 ordered levels, which suggests an ordinal rating task. However, the description gives no explicit when-to-use/when-not rules and does not mention alternatives such as snap_choice or snap_decide.

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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