How to Use the Nationalize MCP in AutoGen

Q: How do I make the Nationalize tool available to my AutoGen agents?

Use the mcpservertools function from the autogen-extmcp package, passing in your server URL. This returns a list of tools, which you can then provide to your AssistantAgent during initialization. The McpToolAdapter handles the rest.

Q: What's a good agent setup for using Nationalize with AutoGen?

A solid pattern is a UserProxyAgent that provides the name, an AssistantAgent that calls predictnationality, and a 'CriticAgent' that evaluates the probability scores. The critic can then decide if the prediction is reliable enough to act on or if the agents need more information.

Let your AutoGen agents debate nationality predictions. Build consensus on user origin before taking action.

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Setup Nationalize with AutoGen

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Debate Nationality Probabilities

The `predict_nationality` tool doesn't give one answer; it returns a list of possibilities with confidence scores. This is perfect for AutoGen. One agent can propose the most likely country, while a 'skeptic' agent can challenge it if the probability is too low. A 'MarketingAgent' might want to use the top result to localize an ad. But a 'ComplianceAgent' can interrupt, pointing out that a 40% probability is too low for automated decisions and suggest a human review is needed. The final action is based on a consensus, not a single guess.

Enrich User Profiles via Agent Chat

You can design a conversation where a 'DataEnrichmentAgent' is tasked with building a user profile. When it gets a name, it calls `predict_nationality` and presents the findings to the group. Another agent, maybe a 'FraudDetectionAgent,' could use that same nationality data for its own analysis. It might cross-reference the predicted nationality with the user's IP address location, flagging discrepancies for the group to discuss. The tool's output becomes a shared fact in the agent conversation.

Let Your AutoGen MCP Server Agents Negotiate

This isn't just about calling a tool. It's about what happens *after*. An 'OnboardingAgent' might use `predict_nationality` to suggest setting the account language. But a 'UserExperienceAgent' could argue against it, stating that assuming language based on name is a bad idea and proposing to ask the user directly instead. This MCP server provides the raw data point—the nationality prediction. Your AutoGen framework turns that data point into the subject of a negotiation. The system's final behavior is more robust because it's the result of different agent perspectives weighing the evidence.

Setup guide

Set up Nationalize 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 Nationalize 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="Nationalize_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Use the `mcp_server_tools` function from the `autogen-ext[mcp]` package, passing in your server URL. This returns a list of tools, which you can then provide to your `AssistantAgent` during initialization. The `McpToolAdapter` handles the rest.

Yes, any agent in the group chat that has the tool in its function map can call `predict_nationality`. This allows them to independently verify results or use the data for their own specific goals within the conversation.

A solid pattern is a `UserProxyAgent` that provides the name, an `AssistantAgent` that calls `predict_nationality`, and a 'CriticAgent' that evaluates the probability scores. The critic can then decide if the prediction is reliable enough to act on or if the agents need more information.

You code their system messages to reflect their roles. For example, instruct one agent to 'propose an action based on the top nationality' and another to 'challenge any action based on a probability below 0.7.' When `predict_nationality` returns a result, their conflicting instructions will trigger a debate.

The name is sent to the Vinkius-managed server for the sole purpose of the `predict_nationality` lookup. The request is handled in a temporary, memory-only container that is destroyed after use. Your data isn't logged or stored on our end.

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