How to Use the Zip MCP in LlamaIndex
Grounding financial data in LlamaIndex using the Zip MCP Server.
Works with every AI agent you already use
…and any MCP-compatible client
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.
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.
Set up Zip MCP in LlamaIndex
Prerequisites
- Python 3.10+ installed
-
llama-index-tools-mcppackage - 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 providesBasicMCPClientandMcpToolSpec. - 2
Connect with BasicMCPClient
Point
BasicMCPClientto 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 Zip MCP tools into nativeFunctionToolobjects that any LlamaIndex agent can use. - 4
Run with any LLM
Create a
FunctionAgentwith the tools and your preferred LLM. SwapOpenAIforAnthropic,Gemini, or any LlamaIndex-supported provider.
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.
Why Choose Vinkius
Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.
Real-time monitoring
Live
visibility into every interaction
Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.
Built-in savings
60%
lower AI costs
Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.
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One
place for every integration
Every tool your AI connects to, managed from a single screen. One account, complete control.
Common questions about Zip MCP in LlamaIndex
Use it with your favorite AI tools
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