How to Use the Eurostat Discovery — Dataset Catalog Explorer MCP in AutoGen

Q: How can I create two agents that use Eurostat Discovery — Dataset Catalog Explorer to collaborate in AutoGen?

You'd create two AssistantAgent instances. Register the Eurostat tools with both, then initiate a chat between them with a task that requires data, like "Figure out the trade balance for Italy."

Q: What's the setup for adding the Eurostat Discovery — Dataset Catalog Explorer tools to an AutoGen AssistantAgent?

Use the autogen-extmcp package to get the mcpservertools function. Point it at the server's URL, and it will return a list of tools. You just pass that list into your AssistantAgent constructor.

Assemble agent teams that debate and analyze EU economic data using AutoGen. Let them collaborate to find the right answers.

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Setup Eurostat Discovery — Dataset Catalog Explorer with AutoGen

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Agents Debate Using Live EU Data

This isn't about one agent finding one answer. With AutoGen, you create a team. An "Analyst" agent uses `search_datasets` to find a dataset on inflation. A "Critic" agent then asks, "Does that include energy prices? Use `get_dataset_metadata` to check." This conversational approach lets agents refine their strategy. They use the tools from this MCP server as facts in their debate, pushing each other to find the most accurate and relevant European statistics.

Challenge and Verify Data Points

Build a self-correcting system. One agent might pull data with `get_dataset` and make a claim. You can program a second "Validator" agent to independently verify that claim. It can re-run the query or use `get_dataset_metadata` to check the data's scope and frequency. This adds a layer of trust. Instead of just accepting the first result, your agent group can reach a consensus that the data is sound before presenting a final conclusion.

Automate Research with an AutoGen MCP Server

Assign a high-level goal, like "Summarize the key demographic shifts in Eastern Europe since 2015." Your multi-agent system takes it from there. One agent acts as a project manager, delegating tasks to others. A "Researcher" agent uses `search_datasets` to locate population and migration datasets. A "Data-Puller" agent executes the `get_dataset` calls. Finally, a "Writer" agent synthesizes the information into a report. It’s an entire workflow, orchestrated through agent conversation.

Setup guide

Set up Eurostat Discovery — Dataset Catalog Explorer 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 Eurostat Discovery — Dataset Catalog Explorer 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="Eurostat Discovery — Dataset Catalog Explorer_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Eurostat Discovery — Dataset Catalog Explorer 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="Eurostat Discovery — Dataset Catalog Explorer_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Eurostat Discovery — Dataset Catalog Explorer 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 Eurostat. 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 Eurostat Discovery — Dataset Catalog Explorer MCP in AutoGen

You'd create two `AssistantAgent` instances. Register the Eurostat tools with both, then initiate a chat between them with a task that requires data, like "Figure out the trade balance for Italy."

Yes, that's a central pattern in AutoGen. The first agent would call `search_datasets` and state the resulting code in the chat. The second agent would see that message and use the code to call `get_dataset`.

Use the `autogen-ext[mcp]` package to get the `mcp_server_tools` function. Point it at the server's URL, and it will return a list of tools. You just pass that list into your `AssistantAgent` constructor.

Absolutely. This is a perfect use case for agent collaboration. One agent can call `get_dataset_metadata`, list the available dimensions in the chat, and the other agent can then construct a precise `get_dataset` call with the correct filters.

The server only handles the data for the duration of the API call inside a secure sandbox. However, AutoGen conversations are often logged. The European Union statistics your agents retrieve will be part of those logs, so be sure to manage your log files appropriately.

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