How to Use the AwardWallet MCP in LlamaIndex

Q: Does LlamaIndex support AwardWallet itineraries?

Yes, you can pull your trip details using getuseritineraries. LlamaIndex handles the structured data and makes it searchable.

Q: Can I query AwardWallet accounts via LlamaIndex?

You can. By calling getuseraccounts, you provide the agent with a list of your loyalty programs. The agent then searches this list to answer your specific questions.

Index your travel data into LlamaIndex for grounded semantic search.

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Connect AwardWallet MCP to LlamaIndex

Create your Vinkius account to connect AwardWallet 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 AwardWallet with LlamaIndex

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Ground LlamaIndex queries in AwardWallet data

The server uses `get_user_accounts` to fetch your current balances. LlamaIndex then indexes this live data so you can ask natural language questions about your points. Stop guessing your totals. You query the index and get answers based on the real values returned by `get_account_details`.

Build RAG apps with AwardWallet

Your agent calls `get_user_itineraries` and feeds the result into a vector store. This creates a searchable knowledge base of your upcoming travel plans. You combine these API results with local documents to provide context. The system uses `get_user_timeline` to ground the response in actual dates and locations.

Search historical AwardWallet records

Use `get_account_history` to pull long-term data for your index. LlamaIndex makes this information available for semantic search across previous trips. Your agent doesn't just read the data; it understands the context of your rewards. It uses `list_loyalty_providers` to categorize the information accurately.

Setup guide

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

Use the McpToolSpec to load the tools into your agent. Once you run the tools, you can ingest the output into your vector store.

Yes, you can pull your trip details using `get_user_itineraries`. LlamaIndex handles the structured data and makes it searchable.

You can. By calling `get_user_accounts`, you provide the agent with a list of your loyalty programs. The agent then searches this list to answer your specific questions.

It provides a clear schema that the agent uses to fetch data. The integration happens through a standard tool spec that maps functions to the agent's capabilities.

Data is transmitted through an encrypted tunnel between the Vinkius sandbox and your environment. Only the specific loyalty account details you request are exposed to the indexing process.

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