How to Use the Modelbit (ML Model Deployments) MCP in AutoGen

Let your AutoGen agents debate model predictions and run inferences from Modelbit.

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Connect Modelbit (ML Model Deployments) MCP to AutoGen

Create your Vinkius account to connect Modelbit (ML Model Deployments) 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 Modelbit (ML Model Deployments) with AutoGen

Ask AI about this MCP

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Perplexity

Consensus-driven model execution in AutoGen

The `get_inference` tool allows your multi-agent system to run predictions during their conversation. One agent can request a prediction, and another can audit the input parameters. This collaborative setup ensures high-quality inputs. The supervisor agent checks the formatting, the executor agent calls the MCP Server, and the analyst interprets the score.

Automated schema conversion for multi-agent setups

This MCP Server uses standard schemas that AutoGen maps automatically. You do not need to write manual translation layers for your agents to understand the tool. The adapter handles the conversion behind the scenes. Your agents read the tool definition, know exactly what parameters the model expects, and pass them correctly.

Streamable HTTP transport for reliable agent messaging

The server uses streamable HTTP transport to keep agent conversations flowing. This setup prevents timeouts when running complex deep learning models that take longer to compute. Your AutoGen framework manages the connection pool. It keeps the channels open so agents can pass inference payloads and receive responses without losing context.

Setup guide

Set up Modelbit (ML Model Deployments) 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 Modelbit (ML Model Deployments) 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="Modelbit (ML Model Deployments)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Modelbit (ML Model Deployments) 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="Modelbit (ML Model Deployments)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Modelbit (ML Model Deployments) data")
    print(result.messages[-1].content)

Get your connection token →

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Common questions about Modelbit (ML Model Deployments) MCP in AutoGen

Use the MCP tool adapter with streamable HTTP parameters. Pass the resulting tool list directly to your AssistantAgent constructor.

Yes. The server handles concurrent requests, allowing different agents in your group chat to call the inference tool as needed.

The MCP Server passes the raw API error back to the calling agent. The agent can then discuss the error and attempt to fix the input payload.

No. Vinkius manages the authentication centrally. Your agents use a single endpoint token to execute the tools.

Yes. All prediction payloads are processed within ephemeral V8 isolates. The transmission is fully encrypted, and data is destroyed immediately after the tool execution completes.

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