How to Use the Razorpay MCP in LlamaIndex

Q: Can LlamaIndex see my Razorpay transaction history?

Yes, by calling listpayments and listsettlements. The agent retrieves these records and adds them to the searchable index.

Index your live Razorpay data into LlamaIndex to query transaction history with semantic search.

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Connect Razorpay MCP to LlamaIndex

Create your Vinkius account to connect Razorpay 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 Razorpay with LlamaIndex

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Query Razorpay status via LlamaIndex

Convert raw API responses into a searchable knowledge base. You no longer need to dig through logs to find a specific transaction status. Use `list_payments` to feed the index. You then ask your agent questions about recent volume and get grounded answers.

Ground answers in live financial data

LlamaIndex pulls real-time details through the MCP server. Your agent cites actual transaction IDs retrieved from `get_payment` instead of guessing. This prevents the common issue of outdated information. The agent always checks the live state before answering your query.

Build RAG pipelines with MCP tools

Combine your internal documents with live payment records. Your agent retrieves policy files and checks `list_refunds` to resolve customer tickets. This unified approach keeps your support team informed. They get the context of the policy and the reality of the transaction in one search.

Setup guide

Set up Razorpay 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 Razorpay 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 Razorpay tools.",
)
response = await agent.run("List recent Razorpay data")

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