How to Use the Knoema MCP in AutoGen

Q: How do I give an AutoGen agent the ability to use Knoema tools?

Use the mcpservertools function from the autogen-ext library, pointing it at the Knoema server URL. This returns a list of tools that you can pass directly to your AssistantAgent constructor in the tools parameter.

Q: How does AutoGen handle errors from a Knoema MCP Server call?

The McpToolAdapter handles the interaction. If a tool call fails, the error is returned as a message in the agent conversation. You can then have another agent (or the user proxy) inspect the error and decide how to proceed.

Q: Is it possible for my AutoGen agents to find Knoema data on specific topics?

Definitely. An agent can use the listdatatopics tool to get a list of all available topics, like 'Agriculture' or 'Economy'. It can then use that information to perform a more targeted search with the searchdatasets tool.

Let your AutoGen agents debate economic policy using live, verifiable data from Knoema.

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Setup Knoema with AutoGen

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Assemble a Team of Analyst Agents

With AutoGen, you create a conversation between specialized agents. You can have a "Scout" agent whose only job is to use `search_datasets` and `list_data_topics` to find promising datasets on Knoema. It then passes the dataset IDs to a "Data-Wrangler" agent. That second agent takes the IDs and uses `get_data_series` to pull the raw numbers. This division of labor lets you build a robust, multi-agent system where each agent has a clear responsibility in the data gathering process. It's more reliable than a single, monolithic agent.

Debate Economic Scenarios with Real Data

This is where AutoGen shines. An "Economist" agent can propose a theory, pulling data via `get_data_series` to back it up. But then a "Skeptic" agent can enter the conversation, challenge the conclusion, and run its own queries to find conflicting evidence. The Skeptic might use `list_dataset_regions` to find a country where the theory doesn't hold, presenting that data as a counterpoint. The agents go back and forth, using fresh Knoema data in their arguments until they reach a more nuanced conclusion. This MCP server provides the facts for their debate.

Automate Data Validation with an Agent Crew

You can set up a workflow where one agent pulls data, but another has to sign off on it. A "Fetcher" agent might use `get_latest_dataset_data` to get new numbers. Before the data is used, an "Auditor" agent automatically calls `get_dataset_metadata` to check the dataset's source and last update time. If the source is untrustworthy or the data is too old, the Auditor agent rejects it and tells the Fetcher to try again. This creates an automated quality-control loop for the data you pull from the Knoema MCP server.

Setup guide

Set up Knoema 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 Knoema 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="Knoema_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Use the `mcp_server_tools` function from the `autogen-ext` library, pointing it at the Knoema server URL. This returns a list of tools that you can pass directly to your `AssistantAgent` constructor in the `tools` parameter.

Yes, that's the ideal way to use AutoGen. You'd create two agents. The first one finds relevant dataset IDs using `search_datasets` and passes them to the second agent in the conversation, which then uses `get_data_series` to work with the data.

The `McpToolAdapter` handles the interaction. If a tool call fails, the error is returned as a message in the agent conversation. You can then have another agent (or the user proxy) inspect the error and decide how to proceed.

Definitely. An agent can use the `list_data_topics` tool to get a list of all available topics, like 'Agriculture' or 'Economy'. It can then use that information to perform a more targeted search with the `search_datasets` tool.

The tool call results — like raw time-series data or dataset metadata — are shared as messages between your agents. The Vinkius MCP server itself is stateless. It processes each request independently and doesn't store the content of your economic data queries or agent conversations.

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