How to Use the BCB Juros — Selic, CDI e Expectativas Focus MCP in AutoGen

Q: How do I initialize the BCB Juros — Selic, CDI e Expectativas Focus MCP Server in AutoGen?

Install autogen-extmcp. Use mcpservertools with your Vinkius URL to fetch the tools, then pass that list directly to your AssistantAgent constructor.

Give your AutoGen multi-agent systems live access to Brazilian interest rates and market forecasts.

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Connect BCB Juros — Selic, CDI e Expectativas Focus MCP to AutoGen

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Setup BCB Juros — Selic, CDI e Expectativas Focus with AutoGen

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Perplexity

Consensus-driven financial modeling

Complex financial decisions require debate. You can spin up a conservative risk agent and an aggressive growth agent in AutoGen. When they argue over a portfolio allocation, they can call `get_cdi` to establish the baseline risk-free rate for Brazilian fixed income. The agents challenge each other using hard data. The growth agent might pull the COPOM target using `get_selic_meta` to justify a specific position. The risk agent counters by analyzing the spread. They negotiate until they reach a mathematically sound consensus.

Native AutoGen MCP Server integration

Wiring up external APIs used to mean writing endless boilerplate. AutoGen's McpToolAdapter handles the schema conversion for this server automatically. You just pass the Vinkius endpoint URL to StreamableHttpServerParams, and the tools are immediately available to your AssistantAgent. The framework translates the BCB parameters into native Python types. Your agents instantly understand how to request the daily effective Selic rate via `get_selic_diaria`. They read the tool description and know exactly when to invoke it during a conversation.

Debating future market conditions

Forecasting is inherently uncertain, making it perfect for multi-agent deliberation. One agent calls `get_expectativas_selic` to grab the Monday Focus Report. It presents the median and maximum projected rates to the group. A secondary macro-analyst agent reviews those projections. It criticizes the median forecast based on recent inflation reports, arguing that the maximum projection is more realistic. The user watches the system deliberate over future economic conditions before delivering a final strategy.

Setup guide

Set up BCB Juros — Selic, CDI e Expectativas Focus 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 BCB Juros — Selic, CDI e Expectativas Focus 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="BCB Juros — Selic, CDI e Expectativas Focus_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent BCB Juros — Selic, CDI e Expectativas Focus data")
print(result.messages[-1].content)

Get your connection token →

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="BCB Juros — Selic, CDI e Expectativas Focus_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent BCB Juros — Selic, CDI e Expectativas Focus 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 Banco Central do Brasil. 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 BCB Juros — Selic, CDI e Expectativas Focus MCP in AutoGen

Install autogen-ext[mcp]. Use mcp_server_tools with your Vinkius URL to fetch the tools, then pass that list directly to your AssistantAgent constructor.

Yes. You provide the tool list to any agent that needs it. A risk manager and a portfolio allocator can both independently request the CDI rate during their debate.

Vinkius manages these servers via HTTP. You will use StreamableHttpServerParams to connect your local multi-agent setup to the remote endpoint.

AutoGen relies on the tool descriptions provided by the server. The descriptions clearly distinguish between the COPOM target and the daily effective rate, allowing the LLM to pick the correct one for its specific argument.

Your multi-agent debates remain entirely private. The MCP connection is authenticated via a single endpoint token, and the ephemeral sandbox only processes requests for public Focus and Selic data. The server has no memory of your internal prompts.

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