How to Use the Temperature Converter for Cooking MCP in AutoGen

Debate your cooking settings using AutoGen and this MCP.

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Connect Temperature Converter for Cooking MCP to AutoGen

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Setup Temperature Converter for Cooking with AutoGen

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Multi-agent temperature debate

Let your agents argue over the correct oven setting for a specific dish. This MCP provides the hard data they need to settle the dispute. One agent calls `celsius_from_fahrenheit` while another verifies the result to ensure the recipe remains accurate.

Gas mark verification

Agents challenge each other to ensure the selected gas mark matches the required heat level. This prevents cooking errors during complex tasks. `oven_settings_from_gas` acts as the source of truth that agents use to reach a consensus.

Automated consensus building

Your agents work together to finalize a recipe plan. They use this MCP to ensure every thermal conversion is accounted for. `fahrenheit_from_celsius` is used by the agents to validate their final output before they present it to you.

Setup guide

Set up Temperature Converter for Cooking 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 Temperature Converter for Cooking 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="Temperature Converter for Cooking_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Temperature Converter for Cooking 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="Temperature Converter for Cooking_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Temperature Converter for Cooking data")
    print(result.messages[-1].content)

Get your connection token →

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