How to Use the TimezoneDB MCP in AutoGen

Build debate-ready agents in AutoGen. Resolve scheduling conflicts with the TimezoneDB MCP Server and consensus-driven logic.

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Setup TimezoneDB with AutoGen

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Consensus on time offsets

Let your AutoGen agents debate the correct time for a meeting across zones using `get_time_by_zone`. They negotiate until they reach agreement. One agent checks the time while another verifies the shift. This multi-agent verification process eliminates scheduling errors.

Verify DST through debate

Use `get_dst_status` as a tool for your security and operations agents. They challenge each other's assumptions about local daylight saving rules. This confirms the status before finalizing any cross-region plans. Your agents converge on a decision that accounts for current regional laws.

API health monitoring agents

Assign a monitoring agent to run `check_api_status` periodically. It ensures the team is aware of any downtime before starting a task. Your system adapts to API availability automatically. Agents pause or retry based on the health check result.

Setup guide

Set up TimezoneDB 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 TimezoneDB 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="TimezoneDB_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent TimezoneDB 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="TimezoneDB_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent TimezoneDB 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 TimezoneDB. 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 TimezoneDB MCP in AutoGen

Use the MCP tool adapter to register the server. Your agents gain access to the full suite of time tools for their conversations.

It does. Agents can delegate time lookups to each other, using the tool results to build a shared understanding of global schedules.

It can. By providing objective data, the tools give your agents a common ground to resolve disputes about time and location.

Your agents can discuss the error output from the API. They can propose alternative strategies if a specific lookup fails during a session.

The server provides only the requested time or status metrics. Your agent conversation logs record these results as part of the decision-making process.

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