How to Use the Travelport MCP in LlamaIndex
Ground AI answers in real travel data using Travelport with LlamaIndex.
Works with every AI agent you already use
…and any MCP-compatible client
Connect Travelport MCP to LlamaIndex
Create your Vinkius account to connect Travelport 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.
Create a searchable index of hotel rules and reservations.
LlamaIndex indexes the output from `get_hotel_rules` and reservation lookups like `retrieve_hotel_reservation`. Instead of just seeing raw JSON, you can query these results semantically—ask 'What is the cancellation policy for this property?' and get a grounded answer. This capability means your RAG applications aren't guessing; they pull facts directly from the MCP Server's structured data.
Build knowledge bases from live search results.
Need to know about nearby options? Running `search_stays_by_location` populates a searchable index. When your agent queries this, it combines the API output with any internal documents you provide. It's contextual search at its best. This is how LlamaIndex turns transient tool outputs into permanent, queryable knowledge.
Query payment validation and pricing history.
You can index the outcomes of payment tools like `validate_card` or `price_offer_reference`. This allows users to ask questions such as 'What was the last known reference price for this air offer?' without needing an active connection. The data persists in your knowledge base.
Set up Travelport MCP in LlamaIndex
Prerequisites
- Python 3.10+ installed
-
llama-index-tools-mcppackage - 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 providesBasicMCPClientandMcpToolSpec. - 2
Connect with BasicMCPClient
Point
BasicMCPClientto 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 Travelport MCP tools into nativeFunctionToolobjects that any LlamaIndex agent can use. - 4
Run with any LLM
Create a
FunctionAgentwith the tools and your preferred LLM. SwapOpenAIforAnthropic,Gemini, or any LlamaIndex-supported provider.
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 Travelport tools.",
)
response = await agent.run("List recent Travelport data") Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Travelport. 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.
Why Choose Vinkius
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Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.
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lower AI costs
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Common questions about Travelport MCP in LlamaIndex
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