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How to Use the Zip MCP in LlamaIndex

Grounding financial data in LlamaIndex using the Zip MCP Server.

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LlamaIndex

Connect Zip MCP to LlamaIndex

Create your Vinkius account to connect Zip 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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Indexing Payment Flows with LlamaIndex

The `create_zip_checkout` tool lets you initialize a payment session, and critically, its output becomes searchable knowledge. You can query past sessions to recall specific amounts or currencies. This turns live transaction data into part of your RAG index, letting the agent ground answers in actual API calls.

Monitoring Zip Order Status for LlamaIndex

`get_zip_order_status` reads an order's current state. Indexing these status checks allows you to build a knowledge base that tracks whether an order was Authorized, Captured, or if it failed. If the index shows a pattern of failures, LlamaIndex can retrieve and synthesize that information for review.

Completing Payments via MCP Server

You use `authorize_zip_order` to lock funds, then capture them with `capture_zip_payment`. Both tool outputs are added to the index. This lets you query not just 'what happened,' but 'how did we complete this specific payment?' It's all part of a unified, queryable knowledge base.

Setup guide

Set up Zip MCP in LlamaIndex

Prerequisites

  • Python 3.10+ installed
  • llama-index-tools-mcp package
  • Active Vinkius subscription with a valid endpoint token
  1. 1

    Install dependencies

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

  2. 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. 3

    Convert to LlamaIndex tools

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

  4. 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 Zip tools.",
)
response = await agent.run("List recent Zip data")

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Zip. 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 Zip MCP in LlamaIndex

You run the `get_zip_order_status` tool and index the results. Later, your agent queries this index to get historical context on any given Zip order.
Yes. Calling `refund_zip_payment` adds records of the refund event to your vector store, allowing you to search and retrieve details about past financial adjustments.
Absolutely. Using `create_zip_checkout` initializes a session whose parameters (amount, currency) are recorded in your searchable index for later reference.
If you use `void_zip_payment` and include the result in your index, LlamaIndex can recall that specific action, providing verifiable data points for auditing.
This server handles payment lifecycle data, including order IDs, transaction statuses, and refund amounts. This is what gets indexed into the knowledge base.

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