How to Use the Gelato MCP in AutoGen

Q: How do I load the Gelato MCP inside AutoGen?

Install autogen-extmcp. You use mcpservertools with a StreamableHttpServerParams configuration, and the adapter automatically converts the endpoints for your AssistantAgent.

Q: How do AutoGen agents handle Gelato catalog updates?

An agent can run listproductcatalogs periodically to detect new inventory. It then informs the rest of the agent swarm that new print-on-demand products are available.

Build multi-agent systems that debate and execute print-on-demand fulfillment via this MCP Server.

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Connect Gelato MCP to AutoGen

Create your Vinkius account to connect Gelato to AutoGen and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Gelato with AutoGen

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Perplexity

Multi-Agent Order Processing

The `create_print_order` tool becomes an action that your AutoGen agents negotiate before executing. A finance agent checks the margins, while an operations agent formats the payload. Before finalizing the MCP transaction, a separate agent invokes `get_shipping_quote`. The group debates whether to upgrade to express shipping based on the cost, reaching a consensus before the final API call.

Autonomous Tracking Monitors

Assign an AutoGen agent to exclusively monitor the `get_order_status` tool. This agent polls the endpoint on a schedule and reports back to a central coordinator agent. If a package stalls, the coordinator instructs a support agent to pull the tracking history via `get_order_shipments`. The agents collaboratively draft a customer apology email based on the exact carrier delays.

Validate API Integrity

Your diagnostic agent runs `verify_api_connection` to ensure the Gelato endpoint is reachable before starting a massive batch job. This prevents the system from failing halfway through a complex multi-agent workflow. If the connection drops, the agent can run `cancel_print_order` on pending jobs to prevent duplicate submissions. The agents communicate the failure state to the user and halt further processing.

Setup guide

Set up Gelato MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Gelato tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Gelato_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Gelato data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Gelato_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Gelato data")
    print(result.messages[-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Gelato. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Gelato MCP in AutoGen

Install `autogen-ext[mcp]`. You use `mcp_server_tools` with a `StreamableHttpServerParams` configuration, and the adapter automatically converts the endpoints for your `AssistantAgent`.

Yes. You pass the same tool list to any agent that needs it. They share the same Vinkius endpoint token to interact with the API.

An agent can run `list_product_catalogs` periodically to detect new inventory. It then informs the rest of the agent swarm that new print-on-demand products are available.

The tool execution happens in the cloud via Vinkius. As long as your local LLM can output the correct JSON schema for the tool call, the server will process it.

Generating a physical order requires transmitting actual street addresses, customer names, and design asset URLs. The MCP Server processes these strings inside an ephemeral, zero-trust sandbox.

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