How to Use the Expedia Hotels MCP in LlamaIndex

Q: Will LlamaIndex and Expedia Hotels help me find hotels based on reviews?

Absolutely. By indexing the output of getguestreviews, your agent can answer specific questions about which hotels have the best ratings for location or cleanliness.

Q: Can I use Expedia Hotels to store my itinerary in a LlamaIndex index?

You can save the JSON output from getitinerary into your document store. This allows your agent to query your booking history using natural language.

Index your travel data in LlamaIndex. Turn Expedia Hotels results into a queryable knowledge base for your agent.

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Connect Expedia Hotels MCP to LlamaIndex

Create your Vinkius account to connect Expedia Hotels 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 Expedia Hotels with LlamaIndex

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Index property details for your agent

Turn the output of `get_hotel_details` into vector embeddings. Your agent can then query your indexed knowledge base to find specific amenities or policies across multiple properties. This makes your search faster. Instead of calling the API for every single query, your agent references the indexed data to narrow down the right hotels.

Synthesize reviews with LlamaIndex

Use `get_guest_reviews` to build a summary index of traveler experiences. Your agent answers questions about room comfort or staff service by querying the stored review data. It gives you context that a standard search misses. You gain a deeper understanding of property quality because the agent pulls from a history of actual guest feedback.

Query past bookings easily

Store your itinerary history from `get_itinerary` in your index. Your agent can look back at past trips to provide recommendations based on your preferences. It keeps your travel history organized. The agent searches your indexed records to quickly recall details like past hotel names or booking dates.

Setup guide

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

You connect the MCP server and run the tools to fetch data. Once you have the results, you feed them into your LlamaIndex vector store for semantic searching.

Absolutely. By indexing the output of `get_guest_reviews`, your agent can answer specific questions about which hotels have the best ratings for location or cleanliness.

You can save the JSON output from `get_itinerary` into your document store. This allows your agent to query your booking history using natural language.

The server provides the tools, and the agent calls them to fetch live data. LlamaIndex then takes that data and makes it searchable for the agent's reasoning process.

Your booking information is handled in memory during the tool execution. The index only stores the data you choose to save, which stays within your local or cloud vector storage environment.

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