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 LangChain 14 tools — connect in under 2 minutes

Name: NotCo
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 14 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect NotCo through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

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 langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "notco": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using NotCo, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

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 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.

LangChain's ecosystem of 500+ components combines seamlessly with NotCo through native MCP adapters. Connect 14 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

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 LangChain 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 LangChain via MCP

Follow these steps to integrate the NotCo MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 14 tools from NotCo via MCP

Why Use LangChain with the NotCo MCP Server

LangChain provides unique advantages when paired with NotCo through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine NotCo MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across NotCo queries for multi-turn workflows

NotCo + LangChain Use Cases

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

RAG with live data: combine NotCo tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query NotCo, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain NotCo tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every NotCo tool call, measure latency, and optimize your agent's performance

NotCo MCP Tools for LangChain (14)

These 14 tools become available when you connect NotCo to LangChain 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 LangChain

Ready-to-use prompts you can give your LangChain 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 LangChain

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

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

NotCo + LangChain FAQ

Common questions about integrating NotCo MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.