How to Use the BLS Public Data API MCP in AutoGen

Q: How do I configure the BLS Public Data API in AutoGen?

Use the mcpservertools function to pull the tool definitions. Inject them into your AssistantAgent, and the agents handle the rest.

Debate economic trends using BLS Public Data API tools within AutoGen groups.

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Connect BLS Public Data API MCP to AutoGen

Create your Vinkius account to connect BLS Public Data API 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 BLS Public Data API with AutoGen

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Perplexity

AutoGen agents debating BLS Public Data API results

Assign one agent to call `get_bls_timeseries_data` and another to analyze the results. They discuss the findings to reach a consensus on economic shifts. Conflict between agents forces a deeper look at the data. You resolve complex questions by letting your agents challenge each other's interpretations.

Monitor BLS Public Data API with AutoGen

Have a specialized agent run `check_api_status` before starting a conversation. If the service is down, the agent reports the issue to the group before work begins. You prevent stalled group chats caused by missing data. It keeps your multi-agent system running smoothly.

Automated tool schema conversion for AutoGen

The adapter handles the heavy lifting of mapping MCP tool schemas to agent-ready formats. You just pass the server tools to your AssistantAgent. Setup takes minutes because the tool types align automatically. You spend your time building the conversation logic instead of writing glue code.

Setup guide

Set up BLS Public Data API 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 BLS Public Data API 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="BLS Public Data API_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent BLS Public Data API 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="BLS Public Data API_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent BLS Public Data API data")
    print(result.messages[-1].content)

Get your connection token →

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Common questions about BLS Public Data API MCP in AutoGen

Agents communicate the data through the conversation history. One agent pulls the figures, and the others process them as part of their group chat.

Yes, you can grant tool access to any agent in the group. They decide collectively which agent should perform the lookup based on their instructions.

The server supports streamable HTTP transports. You configure the client parameters, and the adapter manages the connection automatically.

Use the mcp_server_tools function to pull the tool definitions. Inject them into your AssistantAgent, and the agents handle the rest.

The server runs in an ephemeral sandbox at the transport layer. Only the requested public labor statistics pass between the service and your agents.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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