How to Use the MoonClerk MCP in LlamaIndex

Q: Can LlamaIndex search through MoonClerk customer lists?

Yes. The agent uses listcustomers to retrieve client profiles and indexes them for fast semantic search. This lets you find specific accounts using natural language.

Q: Does LlamaIndex support caching for MoonClerk data?

You can index the output of tools like listpayments into a persistent vector store. This reduces direct API calls while keeping your search index grounded in real data.

Q: Can I restrict which MCP tools the agent can access?

Yes, you can use the allowedtools filter to limit access to specific endpoints. This ensures your search agent only calls the exact tools it needs for the task.

Index your MoonClerk payment records into LlamaIndex to query your billing history using real-time API data.

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Connect MoonClerk MCP to LlamaIndex

Create your Vinkius account to connect MoonClerk 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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Setup MoonClerk with LlamaIndex

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Query live payment data using LlamaIndex RAG

The `list_payments` tool pulls your transactional history directly into your vector store for semantic search. This lets your agent answer complex questions about revenue trends using actual API data instead of relying on memory. By indexing these payment records, you eliminate the risk of hallucinated financial figures. Your queries return grounded, verifiable answers pulled straight from your live checkout database.

Analyze active checkout forms with semantic search

The `list_forms` tool gathers your checkout page configurations so you can index them alongside your product documentation. This allows your agent to match customer queries with the correct payment link instantly. You can filter these tools using the allowed_tools setting to keep your index clean and focused. It ensures your search applications only access the specific forms relevant to the user's current session.

Verify subscriber profiles via MCP Server tools

The `get_customer` tool retrieves specific client details to help verify billing accounts before answering support queries. Your agent checks the retrieved profile to confirm active subscriptions in real-time. Combining this tool with local customer support documents gives your search engine a complete view of client history. You get accurate answers that merge live account status with historical notes.

Setup guide

Set up MoonClerk MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
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 provides BasicMCPClient and McpToolSpec.
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

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all MoonClerk MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
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 MoonClerk tools.",
)
response = await agent.run("List recent MoonClerk data")

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

Install llama-index-tools-mcp and initialize the BasicMCPClient with your server URL. Convert the client to a tool spec and pass it to your FunctionAgent.

Yes. The agent uses `list_customers` to retrieve client profiles and indexes them for fast semantic search. This lets you find specific accounts using natural language.

You can index the output of tools like `list_payments` into a persistent vector store. This reduces direct API calls while keeping your search index grounded in real data.

Yes, you can use the allowed_tools filter to limit access to specific endpoints. This ensures your search agent only calls the exact tools it needs for the task.

Yes. The MCP Server processes payment and customer records inside an ephemeral, zero-trust sandbox. Your live transaction data is never stored permanently on the server.

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