How to Use the BCB Full — Inteligência Financeira Completa do Brasil MCP in LangChain

Q: How do I connect BCB Full — Inteligência Financeira Completa do Brasil to LangChain?

Install langchain-mcp-adapters and langgraph. Initialize a MultiServerMCPClient pointing to the server URL. Call client.gettools() and pass the resulting list into your agent constructor.

Q: Can LangChain agents analyze historical Selic rates using BCB Full — Inteligência Financeira Completa do Brasil?

Yes. The agent can call getselicmeta for the COPOM target or getselicdiaria for the effective daily rate. It can then chain those results into a math tool to calculate historical yields.

Q: How does the MCP server handle the Focus report data?

The system exposes getexpectativasinflacao and getexpectativasselic. Your agent receives the median, mean, minimum, and maximum projections from financial institutions. It can then use this structured data to forecast future policy decisions.

Build multi-step reasoning pipelines with Brazilian financial data using LangChain.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect BCB Full — Inteligência Financeira Completa do Brasil MCP to LangChain

Create your Vinkius account to connect BCB Full — Inteligência Financeira Completa do Brasil to LangChain and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup BCB Full — Inteligência Financeira Completa do Brasil with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Brazilian Economic Chains in LangChain

`get_ipca` and `get_selic_meta` feed directly into your ReAct agents. You build the pipeline, and the agent decides which Brazilian Central Bank indicator to fetch based on intermediate results. If a user asks about inflation trends, the agent pulls the official target index and compares it to the current interest rate target. The output from one tool becomes the input for the next step in your chain. Your script can call `get_expectativas_inflacao` to get the Focus report data, then pass those median projections into a custom prompt for analysis. Everything runs through LangSmith, giving you full observability over token usage and tool inputs.

Foreign Exchange Multi-Step Pipelines

`get_cotacao_moeda` gives your LangChain pipelines access to over 150 currency quotes directly from the PTAX system. You can chain this with `listar_moedas` so the agent first verifies the exact currency code before requesting the exchange rate. This prevents failed tool calls when users ask for obscure currencies like the Chilean Peso or Thai Baht. Historical analysis works the same way. An agent can pull a full year of daily dollar quotes using `get_dolar_periodo` and feed that array into a Python execution tool for statistical analysis. You maintain persistent context across these complex operations by using the client session manager.

Custom SGS Time Series Access via MCP Server

`get_serie_bcb` exposes over 20,000 time series from the Brazilian Central Bank to your MCP Server setup. Your LangChain agent can search for specific statistical codes and pull raw economic data on demand. This includes everything from the IBC-Br monthly GDP proxy via `get_pib` to specific public debt metrics with `get_divida_pib`. Combining these tools creates autonomous financial research agents. The system can check the daily effective interest rate through `get_selic_diaria`, cross-reference it with PIX transaction volumes using `get_pix_estatisticas`, and write a formatted report. You just pass the tools to `create_agent` and let the framework handle the reasoning loop.

Setup guide

Set up BCB Full — Inteligência Financeira Completa do Brasil MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes BCB Full — Inteligência Financeira Completa do Brasil tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "bcb-full-inteligencia-financeira-completa-do-brasil-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent BCB Full — Inteligência Financeira Completa do Brasil transactions"
    })
    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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect BCB Full — Inteligência Financeira Completa do Brasil now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about BCB Full — Inteligência Financeira Completa do Brasil MCP in LangChain

Install `langchain-mcp-adapters` and `langgraph`. Initialize a `MultiServerMCPClient` pointing to the server URL. Call `client.get_tools()` and pass the resulting list into your agent constructor.

Yes. The agent can call `get_selic_meta` for the COPOM target or `get_selic_diaria` for the effective daily rate. It can then chain those results into a math tool to calculate historical yields.

The system exposes `get_expectativas_inflacao` and `get_expectativas_selic`. Your agent receives the median, mean, minimum, and maximum projections from financial institutions. It can then use this structured data to forecast future policy decisions.

Every tool invocation registers in LangSmith automatically. You see the exact payload sent to the Central Bank API and the raw JSON response. This makes debugging complex chains much easier.

The server fetches public macroeconomic indicators like the IPCA inflation index and PTAX dollar quotes. Vinkius runs the process inside an ephemeral V8 Isolate Sandbox. The container destroys itself after execution, ensuring your request parameters leave no permanent trace.

Use it with your favorite AI tools

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

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript