How to Use the Marqeta MCP in LangChain

Build autonomous payment workflows in LangChain. Chain 31 Marqeta tools into automated card issuing and settlement pipelines.

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Connect Marqeta MCP to LangChain

Create your Vinkius account to connect Marqeta 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 Marqeta with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Automate card lifecycle in LangChain

Chain `create_user` and `create_card` calls directly into your agent's reasoning loop. You define the logic, and the agent executes the sequence without manual intervention. This MCP server handles the heavy lifting of state transitions. Use `create_card_transition` to manage card status changes based on your business rules.

Simulate transactions for testing

Trigger `simulate_authorization` and `simulate_clearing` within your LangGraph nodes to validate payment flows. You'll catch edge cases before they hit your production ledger. Connect these tools to LangSmith to trace every step of the authorization process. It’s the fastest way to debug complex payment logic.

Manage velocity and controls

Inject `create_velocity_control` and `create_auth_control` into your chains to enforce spending limits dynamically. Your agent enforces compliance in real-time as it processes requests. Pair these with `get_balances` to make informed decisions before initiating any `create_gpa_order` requests. You maintain full visibility over capital movement.

Setup guide

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

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Common questions about Marqeta MCP in LangChain

Install the necessary adapters and initialize the client using the HTTP transport. You then pass the tool list directly into your agent constructor to enable Marqeta functionality.

Yes. Each tool acts as a distinct node in your chain, allowing the agent to chain together user creation, funding, and card issuance based on the previous output.

It does. Use the client session method to maintain state across multiple turns, which is essential for tracking long-running card issuance workflows.

Use LangSmith tracing to capture inputs and outputs for every tool call. This provides a clear audit trail of every API request sent to the platform.

Your data stays within the secure sandbox environment during testing. The server acts as a bridge, and you control which tools the agent accesses.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python