How to Use the FRED Tags & Sources — Data Discovery MCP in AutoGen

Let your AutoGen agents debate macroeconomic data using FRED Tags & Sources — Data Discovery.

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Connect FRED Tags & Sources — Data Discovery MCP to AutoGen

Create your Vinkius account to connect FRED Tags & Sources — Data Discovery to AutoGen and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup FRED Tags & Sources — Data Discovery with AutoGen

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Agents debate data using FRED Tags & Sources — Data Discovery

Provide `get_series_by_tags` to your AssistantAgents so they can fetch data during their deliberations. If two agents disagree on an inflation trend, one can pull the latest BLS series to settle the argument. This forces consensus based on hard numbers. The agents negotiate which tags to use, ensuring the data retrieved actually addresses the problem they are solving.

Verify source integrity in AutoGen

Use `list_sources` to give your agents visibility into the 107 official FRED data providers. A quality-control agent can check if the source is reputable before the team proceeds with an analysis. It adds a layer of scrutiny to your multi-agent system. The agents won't rely on unverified data, as they can cross-reference the source list during their conversation.

Explore taxonomy with AutoGen

Assign `search_tags` to your agents to allow them to discover the data landscape themselves. If the user asks for 'housing data', the agents can search for relevant tags to narrow down the search space. They become autonomous in their discovery. The agents discuss which tags are most relevant, leading to a more focused and accurate final data output.

Setup guide

Set up FRED Tags & Sources — Data Discovery 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 FRED Tags & Sources — Data Discovery 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="FRED Tags & Sources — Data Discovery_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent FRED Tags & Sources — Data Discovery 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="FRED Tags & Sources — Data Discovery_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent FRED Tags & Sources — Data Discovery data")
    print(result.messages[-1].content)

Get your connection token →

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Common questions about FRED Tags & Sources — Data Discovery MCP in AutoGen

You pass the tool list from the MCP adapter directly into your AssistantAgent constructor. This gives every agent in your group chat access to the FRED API.

Yes. Since agents in a group chat see the tool outputs, they can build upon each other's findings to reach a consensus.

You define the tool list per agent. You can grant access only to the analysts while restricting the writers.

The McpToolAdapter automatically converts the server schema into a format AutoGen understands, letting your agents execute calls natively.

The tools only process public economic series and metadata. No private data is ever shared between your agents or sent to the FRED API endpoints.

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