How do I update the inventory level for a product?

Use the `update_product_inventory` tool with the unique product ID and the new total quantity. This will update the quantity field in LeafLink.

Can I fulfill an order using the agent?

Yes, use the `update_order_status` tool with the order number and set the action to 'fulfill' to transition the order status.

Is it possible to see my buyer list?

Absolutely. The `list_wholesale_customers` tool retrieves all organizational buyers and dispensary accounts registered in your LeafLink network.

LeafLink MCP Server for Pydantic AI 9 tools — connect in under 2 minutes

Name: LeafLink
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 9 Tools SDK

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

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

asyncio.run(main())

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About LeafLink MCP Server

Connect your LeafLink account to any AI agent to automate your cannabis wholesale operations. This MCP server enables your agent to manage product listings, monitor real-time inventory, and track received orders directly from natural language.

Pydantic AI validates every LeafLink tool response against typed schemas, catching data inconsistencies at build time. Connect 9 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

Order Oversight — List and retrieve detailed information for all wholesale orders received from buyers
Inventory Visibility — Get real-time stock levels and availability for your entire product catalog
Catalog Management — List, retrieve, create, and update products including pricing and metadata
Status Transitions — Move orders through their lifecycle (accept, fulfill, cancel) via simple commands
Partner Tracking — List registered brands and buyers to maintain clear visibility of your wholesale network

The LeafLink MCP Server exposes 9 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 LeafLink to Pydantic AI via MCP

Follow these steps to integrate the LeafLink 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 9 tools from LeafLink with type-safe schemas

Why Use Pydantic AI with the LeafLink MCP Server

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

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

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

LeafLink + Pydantic AI Use Cases

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

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

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

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

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

LeafLink MCP Tools for Pydantic AI (9)

These 9 tools become available when you connect LeafLink to Pydantic AI via MCP:

create_new_product

Requires a JSON body with product details. Add a new product to your wholesale catalog

get_order_details

Get details for a specific order

get_product_details

Get details for a specific product

list_received_orders

List all wholesale orders received

list_wholesale_brands

List all brands in your account

list_wholesale_customers

List all buyers and customers

list_wholesale_products

List all products available in your inventory

update_order_status

g., accept, fulfill, cancel, reject). Transition an order through its lifecycle

update_product_inventory

Update inventory level for a specific product

Example Prompts for LeafLink in Pydantic AI

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

"Show me all active wholesale orders in LeafLink."

"Check the inventory level for 'Sour Diesel Flower 3.5g'."

"Accept the order #ORD-101."

Troubleshooting LeafLink MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

LeafLink + Pydantic AI FAQ

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