Zip MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Zip as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Zip. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in Zip?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Zip MCP Server
Connect your Zip (formerly Quadpay) merchant account to any AI agent to automate your Buy Now, Pay Later (BNPL) workflows. This MCP server enables your agent to initiate checkouts, manage order authorizations, and process captures or refunds directly from natural language interfaces.
LlamaIndex agents combine Zip tool responses with indexed documents for comprehensive, grounded answers. Connect 8 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Checkout Initiation — Create new checkout sessions and retrieve redirect URLs for customers
- Order Management — Authorize and confirm orders across US and Global gateway environments
- Payment Ingestion — Capture authorized funds once goods are shipped to finalize transactions
- Financial Resolution — Process full or partial refunds and void authorized but uncaptured payments
- Status Monitoring — Retrieve real-time status and lifecycle updates for any Zip order
- Account Insight — Access merchant configuration, currency limits, and account-level settings
The Zip MCP Server exposes 8 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Zip to LlamaIndex via MCP
Follow these steps to integrate the Zip MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 8 tools from Zip
Why Use LlamaIndex with the Zip MCP Server
LlamaIndex provides unique advantages when paired with Zip through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Zip tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Zip tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Zip, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Zip tools were called, what data was returned, and how it influenced the final answer
Zip + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Zip MCP Server delivers measurable value.
Hybrid search: combine Zip real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Zip to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Zip for fresh data
Analytical workflows: chain Zip queries with LlamaIndex's data connectors to build multi-source analytical reports
Zip MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect Zip to LlamaIndex via MCP:
authorize_zip_order
Typically used in US gateway integrations. Authorize a payment order
capture_zip_payment
Capture funds for an authorized order
confirm_zip_order
Finalize an authorized order
create_zip_checkout
Requires amount, currency, and redirect URLs. Initialize a new Zip checkout session
get_zip_merchant_config
Retrieve merchant account configuration
get_zip_order_status
g., Authorized, Captured, Cancelled) for a target order. Check the status of a Zip order
refund_zip_payment
Process a refund for an order
void_zip_payment
Void an authorized but uncaptured payment
Example Prompts for Zip in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Zip immediately.
"Create a Zip checkout for $100.00 USD."
"Check the status of Zip order ID '98765'."
"Capture payment for authorized order '98765'."
Troubleshooting Zip MCP Server with LlamaIndex
Common issues when connecting Zip to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpZip + LlamaIndex FAQ
Common questions about integrating Zip MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Zip with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
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GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Zip to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.