How to Use the Stark Bank MCP in LangChain
Build complex financial chains with LangChain for multi-step reasoning.
Works with every AI agent you already use
…and any MCP-compatible client
Connect Stark Bank MCP to LangChain
Create your Vinkius account to connect Stark Bank to LangChain — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.
Key Capabilities
Multi-Step Payment Processing (LangChain)
Use `stark_create_pix_charge` to initiate an immediate payment, and then immediately use `stark_list_pix_charges` to confirm the transaction was logged. Your agent can build a chain that verifies payments in real time. This allows your reasoning pipeline to automate multi-stage processes. For instance, it can check the balance using `stark_get_balance`, then decide whether to generate a payment via `stark_create_transfer` or create an invoice with `stark_create_boleto`.
Automated Reconciliation Chains (MCP Server)
You can write chains that reconcile payments across different systems. Start by calling `stark_list_boletos` to see all outstanding receivables, then use the resulting data to inform a call to `stark_get_statement`. This gives you a complete audit trail. This means your agent doesn't just read data; it passes the output of one tool—like a list of transfers from `stark_list_transfers`—into another tool for processing. It's true, sequential automation.
Corporate Card Management (LangChain)
The agent can handle complex expense workflows in a single chain. First, it calls `stark_create_corporate_card` to issue the card data. Next, it tracks usage by calling `stark_list_pix_charges`. It combines these steps to provide full visibility. Building this logic lets you model actual business decisions: 'If balance is below X and I need Y cash flow, then create a Boleto.' You control the decision path.
Set up Stark Bank MCP in LangChain
Prerequisites
- Python 3.10+ installed
-
langchain-mcp-adapters+langgraphpackages - 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 LangChainBaseToolobjects. - 2
Connect via HTTP transport
Use
MultiServerMCPClientwith"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 Stark Bank tool calls through the MCP protocol. - 4
Run with any LLM
Swap
ChatOpenAIforChatAnthropic,ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.
from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI
async with MultiServerMCPClient({
"stark-bank-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 Stark Bank transactions"
})
print(result["messages"][-1].content) Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Stark Bank. 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
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Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.
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lower AI costs
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Common questions about Stark Bank MCP in LangChain
Use it with your favorite AI tools
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