How to Use the Banco Inter MCP in LangChain

Q: Can LangChain automate my Banco Inter statement analysis?

Yes, it pulls your account history using intergetstatement and processes it immediately. You can write logic to flag specific transactions or summarize spending patterns.

Chain Banco Inter operations directly in LangChain agents for automated financial workflows.

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Connect Banco Inter MCP to LangChain

Create your Vinkius account to connect Banco Inter 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.

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Setup Banco Inter with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain financial operations in LangChain

Feed your Banco Inter account data directly into reasoning chains. You can trigger `inter_create_pix_billing` and pipe the response into subsequent logic without manual intervention. This MCP Server maps every action to a programmable link. Your agent decides if it needs a balance check via `inter_get_balance` before initiating a transfer.

Observe Banco Inter state in LangSmith

Trace every interaction with your bank account through LangSmith. You get full visibility into how `inter_get_statement` results affect your agent's internal decision logic. Debugging becomes trivial when you see the exact input sent to `inter_list_boletos`. The chain logs every step of the conversation for your review.

Multi-server aggregation for LangChain

Combine your banking tools with databases and other APIs. Your LangChain agent handles `inter_send_pix` while simultaneously pulling metadata from external storage providers. This setup keeps your financial logic modular. You define the sequence once and let the agent handle the execution flow across different services.

Setup guide

Set up Banco Inter 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 Banco Inter 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({
    "banco-inter-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 Banco Inter transactions"
    })
    print(result["messages"][-1].content)

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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 Inter. 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 Banco Inter MCP in LangChain

Install the required adapters and point the client at your Vinkius endpoint. You initialize the connection once, then pass the tools directly into your agent constructor.

Yes, it pulls your account history using `inter_get_statement` and processes it immediately. You can write logic to flag specific transactions or summarize spending patterns.

It supports stateless calls, but you should use client sessions for persistent context. This keeps your banking history accessible throughout long-running tasks.

The agent receives an error response directly in the chain. You can build error-handling nodes to retry or alert you when an operation like `inter_create_boleto` doesn't go through.

Your financial records never touch our servers. We use an ephemeral sandbox to relay your account credentials and transaction details through a secure, encrypted tunnel.

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