How to Use the Namsor MCP in AutoGen

Q: How do I give my AutoGen agents access to Namsor tools?

After installing autogen-extmcp, use the mcpservertools function with your server URL. This returns a list of tools you can pass directly into your AssistantAgent's constructor.

Q: How does AutoGen handle the output from a Namsor tool call?

The McpToolAdapter automatically converts the JSON output from the Namsor MCP Server into a message. The result of a tool call simply becomes the next turn in the agents' ongoing conversation.

Let your AutoGen agents debate and verify demographic data using Namsor's name tools.

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Setup Namsor with AutoGen

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Have Agents Debate Demographic Data

AutoGen is about conversations between specialized agents. You can give one agent the `predict_ethnicity` tool and another the `predict_origin` tool. Then, you pose a question and let them discuss the findings to arrive at a better answer. For example, a "Data Quality" agent might use `predict_gender` and flag names with low confidence scores. A "Marketing" agent could then use `predict_country` to argue for a different segmentation strategy. The final output is a consensus they reached.

Use AutoGen for Consensus-Driven Insights

This MCP server equips your agents to challenge each other. One agent might run a batch of names through `predict_diaspora`, and a second "Auditor" agent could be programmed to spot-check the results, especially for names common to multiple groups. This isn't a simple pipeline; it's a dynamic system. The agents converse, request more data from each other, and can even re-run tools like `parse_full_name` until they agree on an answer.

Assign Specific Tools to Specialist Agents

In AutoGen, you create a team. You can build a "Compliance" agent that only has access to the `parse_full_name` tool to structure data, and a separate "Analytics" agent that gets the `predict_origin` and `predict_country` tools. This lets you enforce separation of concerns at the agent level. The tools from this MCP server become specific capabilities you grant to each member of your autonomous agent team, making sure they stick to their designated roles.

Setup guide

Set up Namsor 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 Namsor 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="Namsor_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

After installing `autogen-ext[mcp]`, use the `mcp_server_tools` function with your server URL. This returns a list of tools you can pass directly into your `AssistantAgent`'s constructor.

Yes. You can equip your entire agent group with all the Namsor tools, or give specific tools to specific agents. This allows for scenarios where agents collaborate on or verify each other's work using the same function.

The `McpToolAdapter` automatically converts the JSON output from the Namsor MCP Server into a message. The result of a tool call simply becomes the next turn in the agents' ongoing conversation.

Yes, that's a core part of AutoGen. You can watch the full conversation log as agents call tools like `predict_gender`, share the results, and debate the implications before they reach a final conclusion.

Each agent's tool call is an independent, secure request to your Vinkius server. Only the name being analyzed is sent for a given call. The ephemeral sandbox architecture ensures that data from one agent's request is completely isolated from another's.

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