NotCo MCP. Molecular Analysis for Plant-Based Formulation.

Yo, check this out: NotCo connects your AI agent straight into Giuseppe, which is their proprietary Food Tech platform. You're running advanced food science simulations here—it’s how you accelerate B2B research and development using nothing but natural language commands.

This thing handles the whole product lifecycle virtually, from screening raw materials to predicting final costs. Forget physical testing; you test recipes right in your agent. It's built for formulators who need to blueprint a recipe before they even touch a lab bench.

When you wanna build something new, you’ll use create_formulation to ask Giuseppe to generate an entire plant-based formula blueprint based on specific constraints and the target product. You can then pull up details on any existing concept using get_formulation, or if you need a list of what's out there, run list_formulations to see all current AI formulations across categories like dairy or meat.

Before you start building, you gotta know your parts. Use list_ingredients to search the molecular database for every available ingredient, and then use get_ingredient on a specific entry to get its full profile—that includes its molecular structure, sensory data, and functional details. You can also run list_suppliers to see which approved vendors you're working with.

Need benchmarks? Use list_nutritional_profiles to pull up target industry standards so you know what your final recipe needs to hit. Similarly, if you want a baseline for tracking progress, use list_projects or create_project to start and manage a structured R&D project.

Let's talk about the science stuff. If you wanna see how good something is nutritionally, run your formula through analyze_nutrition. It predicts the final output—calories, fat, carbs, and more—per 100g. Wanna know what it’ll cost to make? Use estimate_cost to predict the theoretical mass-production price based on global commodity rates for that mix.

You can also check out all the available sensory standards by running list_sensory_profiles.

If you're trying to nail a specific flavor, don't guess. Run search_flavor_matches to find plant combinations that actually replicate a target volatile molecule or aroma. To simulate what it’ll taste like for actual consumers, use run_sensory_test; this runs an AI simulation of the sensory experience on your generated formula. You can also run through list_formulations if you want to compare existing options against these new virtual results.

It's a full molecular toolkit: You start by defining what you need, then you pull data on ingredients and standards using get_ingredient, list_ingredients, and list_nutritional_profiles. Next, you ask the system to build it with create_formulation or check an old one with get_formulation. After that, you test everything: Run through analyze_nutrition for metrics, calculate cost with estimate_cost, match flavors using search_flavor_matches, and predict the taste profile with run_sensory_test.