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

Q: How do I integrate BCB Full — Inteligência Financeira Completa do Brasil with LlamaIndex?

Install llama-index-tools-mcp via pip. Create a BasicMCPClient, wrap it in an McpToolSpec, and call totoollistasync(). You then pass that list directly to your FunctionAgent.

Q: Can LlamaIndex index historical dollar quotes from BCB Full — Inteligência Financeira Completa do Brasil?

The agent can call getdolarperiodo to fetch up to a year of PTAX quotes. It embeds the daily open, close, and intermediate bulletins into your vector store. Users can then run semantic searches against that historical timeline.

Q: How do I filter which Central Bank tools the agent can use?

You can apply the allowedtools filter when configuring the tool spec. This restricts the agent to specific operations, like only allowing getcdi and getselicmeta for a fixed-income application.

Q: Does BCB Full — Inteligência Financeira Completa do Brasil support foreign exchange rates in LlamaIndex?

The getcotacaomoeda tool pulls real-time buy and sell rates for over 150 currencies. Your RAG system can use this to ground currency conversion answers in official Central Bank data.

Index Brazilian Central Bank data directly into your LlamaIndex knowledge base.

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

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Setup BCB Full — Inteligência Financeira Completa do Brasil with LlamaIndex

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Grounded Brazilian Economic RAG

`get_ipca` and `get_inpc` pull official inflation metrics straight into your LlamaIndex vector store. Your RAG application stops hallucinating economic figures because it queries the actual API data. When a user asks about rent adjustments, the system fetches the exact IGP-M index using `get_igpm` and grounds its answer in the real numbers. The framework converts these tool outputs into searchable nodes. You can run `get_cdi` to get the interbank deposit rate and embed that result alongside your internal fixed-income documents. The query engine then searches across both your private PDFs and the live Central Bank data simultaneously.

Market Expectations Indexing

`get_expectativas_selic` feeds the latest Focus Survey projections into your semantic search pipeline. The tool returns the median, mean, minimum, and maximum interest rate forecasts from financial institutions. Your LlamaIndex setup indexes this structured data so users can ask natural language questions about future monetary policy. You combine this with `get_expectativas_inflacao` to build a complete macro outlook. By passing these tools to a `FunctionAgent`, the system automatically fetches the latest projections whenever a query requires forward-looking data. The agent relies on the Central Bank's weekly publication rather than outdated training data.

Time Series Data via MCP Server

`get_serie_bcb` gives your LlamaIndex application access to 20,000 different statistical series. You can pull the public debt to GDP ratio using `get_divida_pib` or international reserves via `get_reservas`. The framework ingests these data points and makes them available for complex analytical queries. Setting this up requires passing your initialized basic client to `McpToolSpec`. You call the async tool list method and hand the results to your agent. This setup allows your knowledge base to dynamically update its context with live PIX statistics via `get_pix_estatisticas` or monthly GDP proxies through `get_pib`.

Setup guide

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

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all BCB Full — Inteligência Financeira Completa do Brasil MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to BCB Full — Inteligência Financeira Completa do Brasil tools.",
)
response = await agent.run("List recent BCB Full — Inteligência Financeira Completa do Brasil data")

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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 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 Full — Inteligência Financeira Completa do Brasil MCP in LlamaIndex

Install `llama-index-tools-mcp` via pip. Create a `BasicMCPClient`, wrap it in an `McpToolSpec`, and call `to_tool_list_async()`. You then pass that list directly to your `FunctionAgent`.

The agent can call `get_dolar_periodo` to fetch up to a year of PTAX quotes. It embeds the daily open, close, and intermediate bulletins into your vector store. Users can then run semantic searches against that historical timeline.

You can apply the `allowed_tools` filter when configuring the tool spec. This restricts the agent to specific operations, like only allowing `get_cdi` and `get_selic_meta` for a fixed-income application.

The `get_cotacao_moeda` tool pulls real-time buy and sell rates for over 150 currencies. Your RAG system can use this to ground currency conversion answers in official Central Bank data.

This server handles public macroeconomic data like the IBC-Br monthly GDP proxy and trade balance figures. Vinkius enforces a zero-trust architecture where all execution happens in isolated memory. No query parameters or data payloads persist after the connection closes.

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