How do I get an API Key for Gelato?

Log in to your Gelato dashboard, navigate to Developers > API keys, and you can generate a new production or test key there.

What is the difference between an Order ID and a Reference ID?

The Order ID is generated by Gelato, while the Reference ID is your own internal identifier passed during order creation to map the records.

Can I cancel an order via the agent?

Yes, use the 'cancel_print_order' tool. Note that cancellation is only possible if the order status is still 'created' and production hasn't started.

How do I find the correct product UID?

Use the 'list_catalog_products' tool to browse available items. The agent will return the unique 'productUid' needed for order creation.

Gelato MCP Server for CrewAI 12 tools — connect in under 2 minutes

Name: Gelato
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 12 Tools Framework

Connect your CrewAI agents to Gelato through the Vinkius — pass the Edge URL in the `mcps` parameter and every Gelato tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

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

from crewai import Agent, Task, Crew

agent = Agent(
    role="Gelato Specialist",
    goal="Help users interact with Gelato effectively",
    backstory=(
        "You are an expert at leveraging Gelato tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token — get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Gelato "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 12 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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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 Gelato MCP Server

Connect your Gelato account to any AI agent to automate your print-on-demand (POD) lifecycle through the Model Context Protocol (MCP). Gelato enables creators and businesses to produce and ship custom products globally without inventory. This MCP server allows you to manage orders, retrieve product catalogs, and track real-time shipping statuses directly through natural conversation.

When paired with CrewAI, Gelato becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Gelato tools autonomously — one agent queries data, another analyzes results, a third compiles reports — all orchestrated through the Vinkius with zero configuration overhead.

Key Features

Order Management — List all print orders, fetch detailed status metadata, and create new global orders programmatically.
Product Discovery — Access available product catalogs and retrieve detailed specifications for individual items (product UIDs).
Fulfillment Tracking — Retrieve tracking numbers and real-time shipment details for every order in your account.
Pricing & Quoting — Request real-time shipping and production quotes for potential orders across different regions.
Webhook Visibility — List configured webhooks to ensure your internal systems are receiving real-time production updates.
Account Oversight — Verify your account metadata and API connectivity to maintain a seamless production workflow.
Global Fulfillment — Leverage Gelato's massive network of local production partners directly from your chat interface.

The Gelato MCP Server exposes 12 tools through the Vinkius. Connect it to CrewAI 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 Gelato to CrewAI via MCP

Follow these steps to integrate the Gelato MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py — CrewAI auto-discovers 12 tools from Gelato

Why Use CrewAI with the Gelato MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Gelato through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles — one agent researches, another analyzes, a third generates reports — each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass the Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Gelato + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Gelato MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Gelato for raw data, then a second analyst agent cross-references findings and flags anomalies — all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Gelato, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Gelato tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Gelato against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Gelato MCP Tools for CrewAI (12)

These 12 tools become available when you connect Gelato to CrewAI via MCP:

cancel_print_order

Cancel an order

create_print_order

Place new order

get_account_info

Get account identity

get_order_shipments

Track shipments

get_order_status

Get order details

get_product_details

Get product metadata

get_shipping_quote

Request a quote

list_catalog_products

List products in catalog

list_print_orders

List all orders

list_print_webhooks

List webhook configs

list_product_catalogs

List product catalogs

verify_api_connection

Check connection

Example Prompts for Gelato in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Gelato immediately.

"List all my active print orders and their current status."

"Get the shipping details for order 'gelato_12345'."

"List all products in the 'Apparel' catalog (ID: cat_987)."

Troubleshooting Gelato MCP Server with CrewAI

Common issues when connecting Gelato to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts — check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

The Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Gelato + CrewAI FAQ

Common questions about integrating Gelato MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily — when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.