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get_preferences

Read-only

Read the user's food profile: diet style, allergies (a structured list of the major US allergens), foods they like, foods they dislike, and a typical-portion note. Allergies are self-reported and not a safety guarantee; the response includes an allergy_disclaimer. Likes and dislikes are taste preferences, not allergies or restrictions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
likesYes
dislikesYes
allergiesYes
diet_styleYes
portion_noteYes
allergy_disclaimerYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedOutput schema / properties / likes
      Added value: +{
+  "items": {
+    "type": "string"
+  },
+  "type": "array"
+}
    • changedOutput schema / required
      Previous value: -[
-  "diet_style",
-  "allergies",
-  "dislikes",
-  "portion_note",
-  "allergy_disclaimer"
-]New value: +[
+  "diet_style",
+  "allergies",
+  "likes",
+  "dislikes",
+  "portion_note",
+  "allergy_disclaimer"
+]
  2. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "allergies": {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "allergy_disclaimer": {
+      "type": "string"
+    },
+    "diet_style": {
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "dislikes": {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "portion_note": {
+      "type": [
+        "string",
+        "null"
+      ]
+    }
+  },
+  "required": [
+    "diet_style",
+    "allergies",
+    "dislikes",
+    "portion_note",
+    "allergy_disclaimer"
+  ],
+  "type": "object"
+}
  3. Added

TDQS

A4/5.0
Behavior5/5

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

Beyond the annotations' read-only safety profile, the description discloses important behavioral caveats: allergies are self-reported and not a safety guarantee, the response includes an allergy_disclaimer, and likes/dislikes are taste preferences rather than restrictions. These are exactly the kind of semantic warnings an agent needs before relying on the returned data.

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?

The description is three sentences, front-loaded with the core purpose and followed by two clearly relevant caveats. Every sentence earns its place and there is no redundant or filler content.

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

Completeness5/5

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

For a parameterless read-only tool with annotations and an output schema, the description covers the key return semantics and the critical allergy caveat. The output schema can carry structural return details, so no further explanation is required for correct invocation.

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?

The tool takes zero parameters, so there is no parameter semantics to document in the description. The baseline for a parameterless tool is 4, and nothing in the description misleads about input requirements.

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 uses a specific verb ('Read') and resource ('the user's food profile'), then enumerates the returned fields: diet style, allergies, likes, dislikes, and portion note. It is clearly distinguishable from write-oriented siblings like update_preferences in practice, but it does not explicitly name or contrast any alternative tool.

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

Usage Guidelines2/5

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

The description says what is read but gives no explicit when-to-use condition, prerequisites, or comparison to alternatives such as update_preferences or get_usual_foods. An agent must infer that this is the read path for food preferences.

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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