How to Use the Vouchery.io MCP in LlamaIndex

Q: Can I use Vouchery.io with LlamaIndex to check campaign details?

Yes, you can call getcampaign and index the resulting metadata. This allows your RAG application to answer complex questions about a specific marketing campaign's rules.

Q: If I need to find a specific voucher code's details, does Vouchery.io support it with LlamaIndex?

Yes. Running getvoucher populates data that is added to your index. This lets you query the history of that specific voucher code later on.

Q: If I want to audit customer usage, what data type does Vouchery.io use with LlamaIndex?

You can retrieve and index customer profile details, which helps ground your answers in who the user is and their history within Vouchery.io.

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Connect Vouchery.io MCP to LlamaIndex

Create your Vinkius account to connect Vouchery.io 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 Vouchery.io with LlamaIndex

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Build RAG Applications from API Calls

LlamaIndex makes every tool output searchable. If you run `list_redemptions`, the results are indexed into a vector store. This means your agent can answer questions like, 'Why did Customer X redeem voucher Y?' using actual data, not just guessing.

Query Past Coupon Parameters and Configurations

When you index campaign details via `get_campaign`, the knowledge base stores the parameters. Developers can then query past sessions to get answers grounded in the original API output, making auditing simple.

Track Customer History for Contextual Answers

The server lets you index customer data using `list_customers` and detailed records via `get_customer`. Combining this with voucher history allows your agent to provide comprehensive, context-aware answers.

Setup guide

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

LlamaIndex takes the results from the MCP Server and indexes them into a searchable knowledge base. Instead of just executing an action, your agent can query historical API data for answers.

Yes, you can call `get_campaign` and index the resulting metadata. This allows your RAG application to answer complex questions about a specific marketing campaign's rules.

The server deals with several critical data types that can be indexed, including `coupon parameters`, customer records, and active voucher codes.

Yes. Running `get_voucher` populates data that is added to your index. This lets you query the history of that specific voucher code later on.

You can retrieve and index `customer profile` details, which helps ground your answers in who the user is and their history within Vouchery.io.

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