How do I find my Metrc API Keys?

Your Integrator Key is provided by your software vendor. Your User Key is found in your Metrc User Profile under the 'API Keys' section.

Which Base URL should I use?

You must use the API URL corresponding to your state (e.g., https://api-ca.metrc.com for California).

Is my regulatory data secure?

Absolutely. Your token is encrypted at rest and injected securely at runtime.

Metrc MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: Metrc
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

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

    result = await agent.run(
        "What tools are available in Metrc?"
    )
    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 Metrc MCP Server

Connect your Metrc account to any AI agent and take full control of your cannabis track-and-trace compliance through natural conversation.

Pydantic AI validates every Metrc tool response against typed schemas, catching data inconsistencies at build time. Connect 10 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

Facility Oversight — List all licensed facilities and fetch detailed metadata for your operations
Inventory Tracking — Retrieve active items, strains, and inventory packages with real-time status visibility
Supply Chain Management — Monitor plant life cycles, harvest batches, and incoming inventory transfers securely
Compliance Auditing — List active sales receipts and verify unit of measure configurations
Detailed Inspection — Fetch comprehensive metadata for individual packages and state-specific license configurations

The Metrc MCP Server exposes 10 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 Metrc to Pydantic AI via MCP

Follow these steps to integrate the Metrc 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 10 tools from Metrc with type-safe schemas

Why Use Pydantic AI with the Metrc MCP Server

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

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

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

Metrc + Pydantic AI Use Cases

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

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

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

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

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

Metrc MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Metrc to Pydantic AI via MCP:

get_package_details

Get details for a specific package

get_unit_of_measures

g. Grams, Ounces). List all units of measure

list_active_harvests

List active harvests for a facility

list_active_items

List active items for a facility

list_active_packages

List active packages for a facility

list_active_sales

List active sales receipts

list_active_strains

List active strains for a facility

list_facilities

List all licensed facilities

list_incoming_transfers

List incoming inventory transfers

list_tracked_plants

List tracked plants for a facility

Example Prompts for Metrc in Pydantic AI

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

"List all active facilities associated with my Metrc account."

"Show active inventory packages for license 'LIC-12345'."

"Get details for package label 'ABCDEFG1234567'."

Troubleshooting Metrc MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Metrc + Pydantic AI FAQ

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