How do I find my ReciPal API Key?

Log in to your [**ReciPal account**](https://recipal.com/), navigate to your account settings, and you will find your API Key in the integrations or developer section. Copy and paste it below.

Can the agent show serving sizes?

Yes. Every recipe record retrieved by your agent includes serving size metadata and the number of servings per recipe.

Does it support ingredient lists?

Yes. The `get_recipe_details` tool retrieves the full list of ingredients used in a specific recipe, along with their weights.

ReciPal MCP Server for Pydantic AI 4 tools — connect in under 2 minutes

Name: ReciPal
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 4 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect ReciPal through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to ReciPal "
            "(4 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in ReciPal?"
    )
    print(result.data)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About ReciPal MCP Server

Empower your AI agent to orchestrate your entire food manufacturing and recipe auditing workflow with ReciPal, the specialized source for nutritional labeling data. By connecting ReciPal to your agent, you transform complex ingredient analysis into a natural conversation. Your agent can instantly retrieve recipe details, audit calorie counts, and query ingredient lists without you ever touching a labeling portal. Whether you are conducting product research or managing regional dietary constraints, your agent acts as a real-time nutritional consultant, ensuring your data is always verified and precise.

Pydantic AI validates every ReciPal tool response against typed schemas, catching data inconsistencies at build time. Connect 4 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Recipe Auditing — Retrieve high-resolution details for all recipes in your catalog, including names, calorie counts, and serving metadata.
Ingredient Oversight — Audit the available ingredients in the ReciPal database to understand the thematic distribution of components instantly.
Nutritional Intelligence — Query full nutritional breakdowns for specific recipes to assist in deep-dive dietary classification.
Resource Discovery — Retrieve unique recipe identifiers to help you identify relevant markers for your food products.
Operational Monitoring — Check API status to ensure your nutritional research workflow is always operational.

The ReciPal MCP Server exposes 4 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect ReciPal to Pydantic AI via MCP

Follow these steps to integrate the ReciPal MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 4 tools from ReciPal with type-safe schemas

Why Use Pydantic AI with the ReciPal MCP Server

Pydantic AI provides unique advantages when paired with ReciPal through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your ReciPal integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your ReciPal connection logic from agent behavior for testable, maintainable code

ReciPal + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the ReciPal MCP Server delivers measurable value.

Type-safe data pipelines: query ReciPal with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple ReciPal tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query ReciPal and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock ReciPal responses and write comprehensive agent tests

ReciPal MCP Tools for Pydantic AI (4)

These 4 tools become available when you connect ReciPal to Pydantic AI via MCP:

check_api_status

Check if the ReciPal service is operational

get_recipe_details

Get full nutritional and ingredient details for a specific recipe by ID

list_recipal_ingredients

List all ingredients available in the ReciPal database

list_recipal_recipes

List all recipes in your ReciPal account

Example Prompts for ReciPal in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with ReciPal immediately.

"List all my recipes using ReciPal."

"What are the details for recipe ID '12345'?"

"List all ingredients available in ReciPal."

Troubleshooting ReciPal MCP Server with Pydantic AI

Common issues when connecting ReciPal to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

ReciPal + Pydantic AI FAQ

Common questions about integrating ReciPal MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your ReciPal MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.