How to Use the Leonardo.ai (Generative AI & Models) MCP in AutoGen

Q: How do AutoGen agents coordinate polling with Leonardo.ai (Generative AI & Models) using this MCP Server?

One agent calls generateimage and passes the resulting ID to a supervisor agent, which periodically runs getgeneration until the final image is ready.

Q: Can an AutoGen agent group select fine-tuned models from Leonardo.ai (Generative AI & Models)?

Yes. Your planning agent calls listcustommodels to present options, allowing your critic and creative agents to debate and select the best model for the prompt.

Q: How do AutoGen agents handle image-to-image workflows in Leonardo.ai (Generative AI & Models)?

A specialized data agent calls uploadinitimage to stage the base asset, while the prompt agent uses the returned URL to run a guided generation.

Let multiple AutoGen agents debate prompts and collaborate on Leonardo.ai image generation.

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Setup Leonardo.ai (Generative AI & Models) with AutoGen

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Run multi-agent generation debates via this MCP Server

The `generate_image` tool executes prompts that have been negotiated and refined by your AutoGen creative agents. One agent drafts the prompt, another audits it for style consistency, and a third triggers the generation once consensus is reached. After launching the task, the coordinator agent uses `get_generation` to monitor the render status. Once complete, the output is handed back to the critic agent for a quality review.

Delegate model selection to specialized AutoGen agents

The `list_custom_models` tool allows your model-auditing agent to inspect your custom-trained assets and recommend the best fit. Meanwhile, a budget agent checks `get_user` to verify that the generation fits within your current token limits. If the custom models don't fit the requirements, a third agent queries `list_platform_models` to find a public alternative. The agents debate the trade-offs between cost and style before executing the final tool call.

Refine and expand images using AutoGen agent debate

The `create_variation` tool is called when your director agent decides a generated image needs an unzoom adjustment. The agents use this MCP tool to collaboratively formulate the context extension parameters. For complex image-to-image pipelines, one agent calls `upload_init_image` to prepare the source asset. The team then monitors the pipeline, executing `delete_generation` on any failed drafts to keep your workspace clean.

Setup guide

Set up Leonardo.ai (Generative AI & Models) 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 Leonardo.ai (Generative AI & Models) 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="Leonardo.ai (Generative AI & Models)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Leonardo.ai (Generative AI & Models) data")
print(result.messages[-1].content)

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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="Leonardo.ai (Generative AI & Models)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Leonardo.ai (Generative AI & Models) data")
    print(result.messages[-1].content)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Leonardo.ai. 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 Leonardo.ai (Generative AI & Models) MCP in AutoGen

One agent calls `generate_image` and passes the resulting ID to a supervisor agent, which periodically runs `get_generation` until the final image is ready.

Yes. Your planning agent calls `list_custom_models` to present options, allowing your critic and creative agents to debate and select the best model for the prompt.

A specialized data agent calls `upload_init_image` to stage the base asset, while the prompt agent uses the returned URL to run a guided generation.

Yes. A critic agent can review the output of a generation and, if it fails to meet quality standards, instruct the execution agent to call `delete_generation`.

Yes, it is safe. Vinkius manages your API credentials securely inside a zero-trust V8 sandbox, exposing only the safe MCP tool interfaces to your AutoGen runtime.

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