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

Use BCB Full — Inteligência Financeira Completa do Brasil with Pydantic AI for type-safe, validated Brazilian financial data.

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Connect BCB Full — Inteligência Financeira Completa do Brasil MCP to Pydantic AI

Create your Vinkius account to connect BCB Full — Inteligência Financeira Completa do Brasil to Pydantic AI 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 BCB Full — Inteligência Financeira Completa do Brasil with Pydantic AI

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Perplexity

Type-safe financial data for Pydantic AI

Every response from the MCP Server is validated against your Pydantic models. Use `get_ipca15` or `get_igpm` to get structured data that never breaks your type checks. If the Central Bank changes its output format, your agent fails safely with a validation error instead of hallucinating values. It keeps your financial logic predictable.

Strict data validation

Your agent enforces precise types for every index and rate. Using `get_cdi` ensures you get the exact number format your calculator expects. This approach eliminates the risk of silent corruption in your agent's memory. You gain total control over the data that enters your processing pipeline.

Expand your agent's knowledge

Access the full library of 20,000+ time series using `get_serie_bcb`. It provides the raw data for your custom statistical models. Your Pydantic AI agent can dynamically query different indicators based on the task. It provides a robust way to handle complex government data without writing custom parsers.

Setup guide

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

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "bcb-full-inteligencia-financeira-completa-do-brasil-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to BCB Full — Inteligência Financeira Completa do Brasil tools.",
)

result = await agent.run("List recent BCB Full — Inteligência Financeira Completa do Brasil transactions")
print(result.output)

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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 Pydantic AI

You add the MCPToolset to your agent configuration. Since Pydantic AI validates the responses, you get type-safe access to every financial tool provided.

Yes, it supports both Streamable HTTP and SSE transports. You can configure your toolset to use whichever transport matches your running server.

Because you need strict validation for financial data. This setup ensures that every rate or index matches your expected schema before the agent uses it.

Absolutely. Since the toolset is model-agnostic, your data validation remains consistent whether you switch to OpenAI, Anthropic, or a local model.

The server operates behind a zero-trust architecture. It only fetches public economic time series, ensuring no sensitive financial records are exposed during the validation phase.

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