How to Use the Fintoc MCP in LangChain

Q: How do I reconcile Fintoc data using LangChain?

You build a chain that calls listmovements and passes the resulting JSON to a reasoning agent. The agent then compares those movements against your internal records to identify discrepancies.

Q: Can LangChain and Fintoc handle multi-step banking flows?

Yes. You define a sequence where the agent calls getlink to verify access, then executes getaccount to pull current balances for your logic.

Connect Fintoc directly to LangChain agents for automated financial reconciliation and payment processing.

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Connect Fintoc MCP to LangChain

Create your Vinkius account to connect Fintoc 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 Fintoc with LangChain

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Perplexity

Chain Fintoc data into reasoning pipelines

Feed `list_movements` outputs directly into your LangChain agents to analyze transaction history without manual intervention. Each tool call acts as a discrete node in your chain, letting the agent determine the next step based on live bank data. Since LangSmith tracks every input and output, you get full visibility into how your chain handles complex financial logic. This setup prevents data drift by ensuring your agent acts on the most recent account state retrieved from `get_account`.

Automated payment orchestration

Trigger `create_payment_intent` as part of a multi-step workflow when your agent detects specific spending patterns or reconciliation discrepancies. The agent evaluates the transaction context before authorizing any movement of funds. Because this MCP server fits into your existing chains, you can combine Fintoc tools with your internal databases. This allows the agent to verify transaction details against your local records before executing any payment.

Unified financial data access

List institutions and accounts using `list_institutions` and `list_accounts` to map your entire financial structure within one agent. You maintain a single point of control over how your application interacts with bank APIs. By keeping your Fintoc logic inside a LangChain pipeline, you avoid hard-coding API calls. The agent handles the discovery and interaction logic, keeping your codebase clean and focused on your core business rules.

Setup guide

Set up Fintoc 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 Fintoc 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({
    "fintoc-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 Fintoc transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Fintoc MCP in LangChain

You build a chain that calls `list_movements` and passes the resulting JSON to a reasoning agent. The agent then compares those movements against your internal records to identify discrepancies.

Yes. You define a sequence where the agent calls `get_link` to verify access, then executes `get_account` to pull current balances for your logic.

You can store the output of Fintoc tools in a LangChain memory buffer. This keeps historical bank data available for the agent to reference in future turns.

The server uses a single endpoint token for authentication. You should restrict agent access to specific tools like `list_movements` to limit the scope of what your agent can touch.

It accesses transaction movements, account balances, payment intents, and subscription status. All data is handled via encrypted transport and never stored by the Vinkius hosting layer.

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