spoonacular.recipes.analyze
Analyze a recipe by title and ingredient list — returns full nutrition breakdown, dietary labels, and caloric distribution (Spoonacular)
This record as markdown: /tools/io-github-whiteknightonhorse-apibase/spoonacular.recipes.analyze.md
What spoonacular.recipes.analyze does on Apibase
AI agents call spoonacular.recipes.analyze to retrieve information from Apibase without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
| Parameter | Type | Required | Description |
|---|---|---|---|
title | string | Yes | Recipe title (e.g. "Spaghetti Carbonara") |
servings | integer | — | Number of servings (default 1) |
ingredients | array | Yes | List of ingredient strings (e.g. ["200g spaghetti", "100g guanciale", "2 eggs"]) |
instructions | string | — | Cooking instructions as plain text |
Parameters from the server's own tool schema.
Why spoonacular.recipes.analyze is rated Low
Queries recipe data and returns nutritional information with no side effects or modifications.
From the tool's definition Analyze a recipe, returns nutrition breakdown, dietary labels, caloric distribution
Attacks that exploit this kind of access
The rule that runs spoonacular.recipes.analyze safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Apibase, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For spoonacular.recipes.analyze, this is the rule to start with:
spoonacular.recipes.analyze is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Apibase, apply this rule, and every spoonacular.recipes.analyze call is checked against it from then on.
Questions about spoonacular.recipes.analyze
Analyze a recipe by title and ingredient list — returns full nutrition breakdown, dietary labels, and caloric distribution (Spoonacular). It is categorised as a Read tool in the Apibase MCP Server, which means it retrieves data without modifying state.
spoonacular.recipes.analyze accepts 4 parameters: title, servings, ingredients, instructions. Required: title, ingredients. The full parameter table on this page comes from the server's own tool schema.
Register the Apibase MCP server in PolicyLayer and add a rule for spoonacular.recipes.analyze: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Apibase. Nothing to install.
spoonacular.recipes.analyze is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the spoonacular.recipes.analyze rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for spoonacular.recipes.analyze. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
spoonacular.recipes.analyze is provided by the Apibase MCP server (apibase-mcp-client). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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