PingPong MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add PingPong 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 PingPong. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in PingPong?"
)
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 PingPong MCP Server
Empower your AI agent to orchestrate your cross-border financial operations with PingPong, the leading global payment platform for modern e-commerce. By connecting PingPong to your agent, you transform complex account management and fund orchestration into a natural conversation. Your agent can instantly list your global receiving accounts, retrieve real-time balances, monitor transaction histories, and even initiate payouts without you needing to navigate the complex PingPong dashboard. Whether you are managing multiple Amazon stores or distributing funds to global suppliers, your agent acts as a real-time treasury assistant, keeping your capital accurate and your cross-border payments moving.
LlamaIndex agents combine PingPong tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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
- Account Orchestration — List all your PingPong global receiving accounts and retrieve detailed metadata for each.
- Balance Monitoring — Get real-time balance information across multiple currencies and account types.
- Transaction Auditing — Browse transaction histories with full support for filtering by status and currency.
- Payout Control — Initiate fund withdrawals and monitor the real-time status of your payouts.
- Treasury Insights — Retrieve high-level summaries of your global sales and virtual card (VCC) balances.
The PingPong MCP Server exposes 10 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 PingPong to LlamaIndex via MCP
Follow these steps to integrate the PingPong 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 10 tools from PingPong
Why Use LlamaIndex with the PingPong MCP Server
LlamaIndex provides unique advantages when paired with PingPong through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine PingPong tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain PingPong tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query PingPong, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what PingPong tools were called, what data was returned, and how it influenced the final answer
PingPong + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the PingPong MCP Server delivers measurable value.
Hybrid search: combine PingPong real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query PingPong 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 PingPong for fresh data
Analytical workflows: chain PingPong queries with LlamaIndex's data connectors to build multi-source analytical reports
PingPong MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect PingPong to LlamaIndex via MCP:
create_payout
Create a new payout
get_account_details
Get account information
get_balance
Get account balance
get_exchange_rates
Get real-time exchange rates
get_payout_status
Check payout status
get_sales_summary
Get global sales summary
get_vcc_balance
Get virtual card balance
list_accounts
List global accounts
list_store_accounts
). List e-commerce store accounts
list_transactions
List account transactions
Example Prompts for PingPong in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with PingPong immediately.
"List all my PingPong receiving accounts."
"What is my current balance in USD across all accounts?"
"Check the status of payout 'PAY-8821'."
Troubleshooting PingPong MCP Server with LlamaIndex
Common issues when connecting PingPong to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpPingPong + LlamaIndex FAQ
Common questions about integrating PingPong 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 PingPong 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.
AI-first code editor with integrated LLM-powered coding assistance.
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 PingPong to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.