Can I use this to generate a formula for a new plant-based meat?

Yes! Use the `create_formulation` tool to instruct Giuseppe to model a specific animal product (e.g., 'Target: Pulled Pork'). You can pass constraints like 'no soy' or 'must contain pea protein'. Giuseppe will return a mathematically generated formula combining plant extracts that mimic the target's molecular profile.

How does Giuseppe match specific flavors?

The `search_flavor_matches` tool analyzes NotCo's proprietary database. Instead of searching for 'beef flavor', Giuseppe searches for the specific volatile molecular compounds that create the beef flavor, and then finds combinations of seemingly unrelated plants (like pineapple and cabbage) that, when combined mathematically, replicate that exact molecular behavior.

Can I predict the cost of a new formulation before making it?

Absolutely. Once Giuseppe generates a formulation, you can pass its ID to the `estimate_cost` tool. The API cross-references the required plant ingredients with global B2B commodity pricing databases to give you an estimated per-kilogram cost for mass production.

NotCo MCP Server for Pydantic AI 14 tools — connect in under 2 minutes

Name: NotCo
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 14 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect NotCo 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 NotCo "
            "(14 tools)."
        ),
    )

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

asyncio.run(main())

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* 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 NotCo MCP Server

Connect your AI agent directly to Giuseppe, NotCo’s proprietary Food Tech AI platform. Accelerate your B2B food research and development by commanding Giuseppe through natural language.

Pydantic AI validates every NotCo tool response against typed schemas, catching data inconsistencies at build time. Connect 14 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

AI Formulation Generation — Command Giuseppe to analyze the molecular structure of an animal product and generate a plant-based blueprint using combinations from a database of over 300,000 plants
Ingredient Search — Browse and retrieve the molecular, sensory, and functional profiles of thousands of plant extracts and proteins
Flavor & Texture Matching — Use AI computational models to find plant compounds that replicate specific volatile molecules, aromas, and mouthfeels
Nutritional Label Prediction — Run predictive algorithms on custom plant mixtures to estimate the final nutritional values of a recipe before entering the lab
Cost Estimation — Predict the theoretical mass-production cost of an AI-generated formula based on global commodity pricing

The NotCo MCP Server exposes 14 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 NotCo to Pydantic AI via MCP

Follow these steps to integrate the NotCo 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 14 tools from NotCo with type-safe schemas

Why Use Pydantic AI with the NotCo MCP Server

Pydantic AI provides unique advantages when paired with NotCo 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 NotCo integration code

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

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

NotCo + Pydantic AI Use Cases

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

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

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

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

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

NotCo MCP Tools for Pydantic AI (14)

These 14 tools become available when you connect NotCo to Pydantic AI via MCP:

analyze_nutrition

Analyze the nutritional output of a formulation

create_formulation

Request a new AI formulation

create_project

Create a new R&D project

estimate_cost

Predict the mass production cost of a formulation

get_formulation

Get details of a specific AI formulation

get_ingredient

Get complete molecular profile of an ingredient

list_formulations

g., dairy, meat, sauces). List plant-based AI formulations

list_ingredients

Search the plant-based ingredient molecular database

list_nutritional_profiles

List target nutritional benchmarks

list_projects

List active R&D projects

list_sensory_profiles

List standard sensory profiles

list_suppliers

List approved ingredient suppliers

run_sensory_test

Run an AI simulation of a sensory test

search_flavor_matches

Find plant combinations that mimic a target flavor

Example Prompts for NotCo in Pydantic AI

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

"Ask Giuseppe to create a plant-based alternative for condensed milk, with the constraint 'no palm oil'."

"Run a nutritional analysis on formulation ID #CM-882."

"Estimate the mass-production cost for this formulation."

Troubleshooting NotCo MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

NotCo + Pydantic AI FAQ

Common questions about integrating NotCo 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 NotCo MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.