How to Use the Marqeta MCP in LlamaIndex

Q: How do I filter which Marqeta tools LlamaIndex can use?

Use the allowedtools filter during setup. This restricts the agent to specific operations, keeping your integration focused and secure.

Index your payment data with LlamaIndex. Turn Marqeta API outputs into a searchable knowledge base for your AI.

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Connect Marqeta MCP to LlamaIndex

Create your Vinkius account to connect Marqeta to LlamaIndex 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 LlamaIndex

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Index card data in LlamaIndex

Convert `list_cards_by_user` and `get_user` outputs into vector-searchable documents. You can query your entire card program using natural language. This creates a grounded knowledge base for your agent. It no longer guesses; it pulls facts directly from your Marqeta account data.

Query past transactions in LlamaIndex

Use `get_direct_deposit` and balance checks to populate your index with real-time financial records. Your agent provides answers based on actual account history. This approach eliminates hallucinations by grounding responses in live data. You get precise answers about cardholder activity without digging through logs.

Automate RAG with Marqeta tools

Combine the `McpToolSpec` with your existing LlamaIndex pipelines to fetch data dynamically. The agent decides when to pull fresh data from the API. It keeps your index updated with the latest state from Marqeta. You build RAG apps that reflect the current reality of your payment system.

Setup guide

Set up Marqeta MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Marqeta MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Marqeta tools.",
)
response = await agent.run("List recent Marqeta data")

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Common questions about Marqeta MCP in LlamaIndex

Use the MCP client to generate a tool spec. Once converted into a tool list, you pass these directly to your function agent for immediate use.

Yes. By indexing the output of your tools, you create a searchable knowledge base that links user profiles, card details, and transaction history.

Absolutely. By feeding tool outputs into your vector store, the agent retrieves factual data from the platform to inform its responses.

Use the allowed_tools filter during setup. This restricts the agent to specific operations, keeping your integration focused and secure.

The server transmits data through encrypted channels. Only the information you retrieve is indexed, and you manage the scope of what gets stored.

Use it with your favorite AI tools

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

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