How to Use the Amadeus MCP in LlamaIndex

Q: Can LlamaIndex store Amadeus tool outputs as knowledge?

Absolutely. By enabling includeresources, you allow the framework to ingest API results into your vector index for semantic retrieval later.

Ground your LlamaIndex RAG pipelines in live travel data using the Amadeus MCP Server for accurate, indexable results.

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Connect Amadeus MCP to LlamaIndex

Create your Vinkius account to connect Amadeus 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 Amadeus with LlamaIndex

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Index Amadeus results in LlamaIndex

Pipe the output of `search_flights` directly into your vector store. LlamaIndex treats these API responses as searchable knowledge nodes for future queries. This turns transient flight data into a persistent index. You query your past searches to find trends or specific price points without re-running the full API call.

Build grounded RAG apps with Amadeus

Connect `search_hotels` to your FunctionAgent to ground answers in real-time inventory. LlamaIndex retrieves the data and synthesizes it into a coherent summary. Your system avoids hallucinations by pulling facts directly from the source. It verifies current availability against your local document base before suggesting a destination.

Filter tools for targeted travel insights

Use the allowed_tools filter to limit the Amadeus MCP Server to specific functions like `search_airports`. This keeps your LlamaIndex agent focused on relevant tasks. Managing your tool list reduces noise in the reasoning loop. You define the boundaries of what the agent can access, ensuring it only uses the data you want indexed.

Setup guide

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

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Common questions about Amadeus MCP in LlamaIndex

Use the llama-index-tools-mcp package to initialize the client. Convert the tool spec to a list and pass it to your FunctionAgent for immediate use.

Absolutely. By enabling include_resources, you allow the framework to ingest API results into your vector index for semantic retrieval later.

Yes, you can call the tool list asynchronously to keep your application responsive. This is standard for modern RAG pipelines built on this framework.

We use ephemeral sandboxing for all requests. Every interaction between your LlamaIndex app and the server is strictly scoped to your unique endpoint token.

The server provides structured flight, hotel, and activity records. These objects are converted into clean formats that LlamaIndex indexes without exposing your private account keys.

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