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RBV801

recipal-mcp-unofficial

by RBV801

create_recipe

Create a new empty recipe in ReciPal. Use this to build a recipe from scratch; to preserve label settings from an existing recipe, clone it via scale_recipe instead.

Instructions

Create a new empty recipe. POST /recipes. Prefer scale_recipe (clone a fully-configured template) when you need label settings to match existing recipes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fieldsYesOpen key/value object of attributes. Keys are passed straight through to the ReciPal API namespaced under the resource (e.g. {name, package_yield_quantity, package_yield_unit, servings, packages, suggested_serving, sku, preparation, visual_unit_of_measure}). Call get_recipe on an existing recipe first to see the exact attribute names this account uses. Note that tags cannot be set here -- ReciPal accepts the request and silently ignores a tags string, and errors on other shapes. Tags do carry forward through scale_recipe when cloning a tagged template.
as_jsonNoSend JSON instead of form encoding.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.6.2

TDQS

A4.2/5.0
Behavior3/5

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

With no annotations, the description carries the behavioral burden. It discloses that the resulting recipe is 'empty' and that the operation is a POST to /recipes, which signals a non-idempotent create. However, it does not mention required permissions, side effects, or what happens on success or failure, so important behavioral context is still missing.

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 two sentences with no filler. The core action is front-loaded, the endpoint is a useful quick signal, and the alternative guidance is tucked naturally into the second sentence.

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?

The description plus the rich schema is largely complete for invoking the tool: the agent knows what it creates, when to use an alternative, and what fields are expected. The main gap is that there is no output schema and the description doesn't state what the API returns, which could matter for chaining the created recipe into subsequent calls.

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 parameters are already well documented in the input schema. The tool description itself adds no parameter-level detail, but that is acceptable because the schema provides example attribute names and warns about tag behavior.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a clear action and resource: 'Create a new empty recipe' and even gives the endpoint POST /recipes. The word 'empty' distinguishes it from clone-style operations, and the sibling list shows alternatives like create_recipe_shortcut and create_subrecipe, so there is no ambiguity about what this tool creates.

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

Usage Guidelines5/5

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

The description explicitly tells the agent when to prefer an alternative: 'Prefer scale_recipe (clone a fully-configured template) when you need label settings to match existing recipes.' This gives a concrete, decision-relevant condition rather than leaving the agent to infer when scale_recipe should be used.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.