MoonClerk MCP Server for LlamaIndexGive LlamaIndex instant access to 7 tools to Get Customer, Get Form, Get Payment, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add MoonClerk 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 App Connector for LlamaIndex
The MoonClerk app connector for LlamaIndex is a standout in the Money Moves category — giving your AI agent 7 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 MoonClerk. "
"You have 7 tools available."
),
)
response = await agent.run(
"What tools are available in MoonClerk?"
)
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 MoonClerk MCP Server
Connect your MoonClerk account to any AI agent to streamline your payment monitoring and customer oversight. MoonClerk provides a robust API for programmatically retrieving your payment records, customer plan details, and active payment forms.
LlamaIndex agents combine MoonClerk tool responses with indexed documents for comprehensive, grounded answers. Connect 7 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
- Customer Monitoring — List all your customers (plans) and retrieve detailed metadata including status and plan types
- Payment Tracking — Access your full payment history and monitor the status of recurring and one-time transactions
- Form Orchestration — List all your active payment forms and retrieve their public URLs and configurations
- Coupon Intelligence — Access your list of active coupons to understand your promotional landscape
- Real-time Oversight — Get a comprehensive overview of your payment volume and customer growth using natural language commands
The MoonClerk MCP Server exposes 7 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.
All 7 MoonClerk tools available for LlamaIndex
When LlamaIndex connects to MoonClerk through Vinkius, your AI agent gets direct access to every tool listed below — spanning recurring-payments, checkout-pages, subscription-management, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Get customer details
Get form details
Get payment details
List all coupons
List all MoonClerk customers
List all payment forms
List all payments
Connect MoonClerk to LlamaIndex via MCP
Follow these steps to wire MoonClerk into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the MoonClerk MCP Server
LlamaIndex provides unique advantages when paired with MoonClerk through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine MoonClerk tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain MoonClerk tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query MoonClerk, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what MoonClerk tools were called, what data was returned, and how it influenced the final answer
MoonClerk + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the MoonClerk MCP Server delivers measurable value.
Hybrid search: combine MoonClerk real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query MoonClerk 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 MoonClerk for fresh data
Analytical workflows: chain MoonClerk queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for MoonClerk in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with MoonClerk immediately.
"List all active customers in my MoonClerk account."
"Show the last 5 payments received today."
"List all my active payment forms."
Troubleshooting MoonClerk MCP Server with LlamaIndex
Common issues when connecting MoonClerk to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpMoonClerk + LlamaIndex FAQ
Common questions about integrating MoonClerk MCP Server with LlamaIndex.