mcp-supertoinette

Rescale a list of ingredients

scale_ingredients

Read-onlyIdempotent

Rescale French ingredient lines using a factor or from/to serving counts. Each line returns its status: scaled, rounded, or unchanged.

Instructions

Rescale a list of French ingredient lines, offline. Give either 'factor', or 'from_servings' and 'to_servings' together. Each line comes back with what was done to it: 'scaled' when the arithmetic landed exactly, 'rounded' when the value had to move to stay something a kitchen can measure out, and 'unscaled' when the line carries no quantity at all. Nothing is converted between unit systems, and an approximate measure such as a pincée keeps its own size.

Input Schema

TableJSON Schema

Name	Required	Description
`factor`	No	What to multiply the quantities by. Give this, or the two servings counts.
`ingredients`	Yes	The lines to rescale, as the recipe wrote them.
`to_servings`	No	How many it should serve.
`from_servings`	No	How many the list was written for.

Output Schema

TableJSON Schema

Name	Required	Description
`notes`	Yes
`factor`	Yes	What the quantities were multiplied by.
`ingredients`	Yes
`scaled_count`	Yes	Lines whose arithmetic landed exactly.
`rounded_count`	Yes	Lines whose value had to move.
`unscaled_count`	Yes	Lines carrying no quantity.

Schema Changelog

Changes observed during successful MCP inspections.

Changed6 schema fields changedv1.1.0Sep 11, 2026
- removedOutput schema / properties / ingredients / items / properties / amount / anyOf
```
Removed value: -[
-  {
-    "type": "number"
-  },
-  {
-    "type": "null"
-  }
-]
```
- addedOutput schema / properties / ingredients / items / properties / amount / type
```
Added value: +[
+  "number",
+  "null"
+]
```
- removedOutput schema / properties / ingredients / items / properties / amount_max / anyOf
```
Removed value: -[
-  {
-    "type": "number"
-  },
-  {
-    "type": "null"
-  }
-]
```
- addedOutput schema / properties / ingredients / items / properties / amount_max / type
```
Added value: +[
+  "number",
+  "null"
+]
```
- removedOutput schema / properties / ingredients / items / properties / unit / anyOf
```
Removed value: -[
-  {
-    "type": "string"
-  },
-  {
-    "type": "null"
-  }
-]
```
- addedOutput schema / properties / ingredients / items / properties / unit / type
```
Added value: +[
+  "string",
+  "null"
+]
```
First observedv1.0.1Aug 28, 2026

TDQS

A4.2/5.0

Behavior5/5

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

Beyond the readOnly, idempotent, and non-destructive annotations, the description discloses meaningful behavior: per-line outcome statuses ('scaled', 'rounded', 'unscaled'), the fact that no unit-system conversion happens, and that approximate measures keep their own size. This gives the agent a clear model of what the tool will and will not do.

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 compact and front-loaded: the main action is stated first, followed by essential parameter constraints and behavioral outcomes. Each sentence contributes information that is not already visible in the schema or annotations.

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 pure transformation tool with rich annotations age and an output schema, the description covers the essential contextual pieces: input mode selection, result statuses, unit handling, and offline behavior. Nothing critical is missing for an agent to call it correctly.

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 input schema already describes each parameter, so the description does not need to compensate for coverage gaps. It adds real value by explaining the mutual-exclusion relationship between 'factor' and the two servings counts, and clarifies that servings must be supplied together.

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 names a specific resource (French ingredient lines) and a precise operation (rescale), plus the offline qualifier, which clearly sets it apart from the retrieval-oriented sibling tools. It does not explicitly name a competing sibling, so it stops short of the strongest possible sibling differentiation.

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?

It gives solid within-tool usage guidance by requiring either 'factor' or the servings-count pair, and it explains what will happen as a result. However, it provides no explicit guidance on when to choose this tool over the listed sibling tools, leaving tool-selection context implied.

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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