How to Use the Checkout.com MCP in LlamaIndex

Q: Does the Checkout.com server work with LlamaIndex filters?

Yes, you can use the allowedtools filter to restrict which API calls your agent can make. This is useful for limiting read-only access in your RAG setup.

Index your Checkout.com transaction history into LlamaIndex for semantic search and RAG applications.

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Connect Checkout.com MCP to LlamaIndex

Create your Vinkius account to connect Checkout.com 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 Checkout.com with LlamaIndex

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Query your Checkout.com logs with LlamaIndex

Use `list_checkout_payments` to pull transaction history into your local vector store. Your index now understands your payment patterns. This turns raw API data into a knowledge base. You can ask your agent about specific payment trends without writing custom SQL queries.

Ground LlamaIndex answers in live payment data

Call `get_payment_details` to retrieve the latest status for any transaction. LlamaIndex then uses this live data to answer user questions. It stops the agent from guessing. By combining real-time API results with your indexed documents, the answers you get are grounded in reality.

Automate Checkout.com data indexing

Set up a pipeline that pulls from `list_payment_actions` to keep your knowledge base fresh. Your agent always works with the latest audit trail. This keeps your RAG application relevant. You avoid stale data by letting the agent poll the API as part of its indexing routine.

Setup guide

Set up Checkout.com 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 Checkout.com 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 Checkout.com tools.",
)
response = await agent.run("List recent Checkout.com 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 Checkout.com. 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 Checkout.com MCP in LlamaIndex

Install the MCP tools and initialize them with the BasicMCPClient. You then pass these tools to your FunctionAgent to enable data indexing.

Absolutely, once you index the output of the payment list tools. You can perform semantic searches to find transactions based on their descriptions or metadata.

Yes, you can use the allowed_tools filter to restrict which API calls your agent can make. This is useful for limiting read-only access in your RAG setup.

By grounding your agent in live API results, you minimize errors. The agent processes the exact JSON returned by the server, ensuring accuracy in its reports.

The server transmits data through an encrypted Vinkius tunnel. Your customer records are only indexed in your local vector store, never exposed to third-party model training.

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