Recipe Generation for AI Agents

Overview

Recipe Generator uses AI to create detailed recipes based on your request. Simply provide a dish name or description, and it will generate a complete recipe with ingredients, step-by-step instructions, preparation time, cooking time, and serving size.

Live Test Recipe Generation for AI Agents Source →

The tool

Once your client is connected to the VerveContext server, this appears in its tool list as RecipeGenerationforAIAgents. It is read-only and open-world — it fetches and never mutates anything on your side — so most clients call it without asking you to confirm.

{
  "name": "RecipeGenerationforAIAgents",
  "arguments": {
    "name": "chicken fajitas"
  }
}

You do not name the tool yourself; the model picks it. Asking about chicken fajitas in the terms this source covers is enough for it to reach for RecipeGenerationforAIAgents on its own — naming it explicitly also works, and is the way to force the call.

Connecting

One server URL covers every source in the catalog, including this one. Authorization is OAuth: the client opens a browser once, and there is no key to paste into a config file.

{
  "mcpServers": {
    "vervecontext": {
      "url": "https://api.vervecontext.com/v1/mcp"
    }
  }
}

https://api.vervecontext.com/v1/mcp

Per-client setup — Claude, Cursor, VS Code, ChatGPT — is on the MCP setup page.

Arguments

These are the properties on the tool's inputSchema, so a well-behaved client validates them before the call is made. Premium arguments are accepted on every plan but only take effect on plans that include them.

What the model gets back

The result carries a structuredContent object matching the tool's declared outputSchema, so a client reads fields without parsing prose. status is "ok" and error is null on success; a null field means the value was not available for that input, not that the call failed.

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{
  "status": "ok",
  "error": null,
  "data": {
    "name": "Chicken Fajitas",
    "description": "A flavorful and easy-to-make dish with marinated chicken, bell peppers, and onions, served with warm tortillas.",
    "ingredients": [
      "1.5 pounds boneless, skinless chicken breasts, sliced",
      "1 red bell pepper, sliced",
      "1 green bell pepper, sliced",
      "1 large onion, sliced",
      "2 tablespoons olive oil",
      "1 packet (1 ounce) fajita seasoning",
      "1/2 cup water",
      "12 flour tortillas",
      "Optional toppings: sour cream, guacamole, salsa, shredded cheese"
    ],
    "instructions": "1. In a bowl, combine chicken with fajita seasoning and water. Marinate for at least 15 minutes.\n2. Heat olive oil in a large skillet or cast-iron pan over medium-high heat.\n3. Add the chicken and cook until browned and cooked through, about 5-7 minutes.\n4. Add bell peppers and onions to the skillet. Cook until softened, about 5-7 minutes.\n5. Warm tortillas according to package instructions.\n6. Serve chicken and vegetables in warm tortillas with desired toppings.",
    "prep_time": "15 minutes",
    "cook_time": "20 minutes",
    "servings": 4
  }
}

Response fields

Paths are relative to data. Premium fields are absent rather than zeroed on plans that do not include them, so check for presence instead of comparing to 0.

Why ground on it

A model can produce something that looks like this answer from its training data, and be confidently out of date or simply wrong. This source returns the current value in a shape you can check, which is the difference between an answer you can cite and one you have to hedge.

Point an evaluation at description: it is the field most worth pinning a claim to, and it is either present and current or absent — never plausibly invented.

Failure modes

Errors come back as tool errors carrying a sentence the model can act on, not a bare status code. Error handling covers the full list.

A call costs 2 credits each time the tool actually runs; a model that reasons about the tool without calling it costs nothing.

Use cases

Meal Planning Calendars

Meal planning platforms generate weekly dinner schedules by passing suggested dish names and populating shopping lists with the returned ingredient quantities.

Smart Kitchen Displays

When a cook selects a dish on a connected appliance, the interface displays ordered preparation instructions paired with active prep and cook timers.

Grocery Delivery Carts

Supermarket apps turn customer meal requests into full ingredient lists with quantities, adding every required item directly to the digital shopping basket.

Fitness Calorie Trackers

To help users log homemade meals accurately, nutrition logs look up dish concepts to retrieve exact ingredients and estimated serving sizes.

Other ways to use Recipe Generation for AI Agents

Set up Recipe Generation for AI Agents on VerveContext, or reach the same source a different way. Your VerveContext account and credits work on all of them — one key, one balance.

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