Amenitiz is an all-in-one PMS for independent hotels — website builder, booking engine, channel manager, and operations management. Popular across Europe with 10,000+ hotels.

Amenitiz MCP Server for LlamaIndex 8 tools — connect in under 2 minutes

Name: Amenitiz
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 8 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Amenitiz as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Amenitiz. "
            "You have 8 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Amenitiz?"
    )
    print(response)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Amenitiz MCP Server

Connect Amenitiz to any AI agent — the all-in-one PMS for independent hotels.

LlamaIndex agents combine Amenitiz tool responses with indexed documents for comprehensive, grounded answers. Connect 8 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

What you can do

Reservations — Browse bookings with guest details, dates, and payment
Room Types — Categories with capacity, amenities, and base rates
Rooms — Individual room status: clean, dirty, occupied
Availability — Day-by-day openings by room type
Rates — Seasonal pricing and promotions
Guests — Guest database with visit history and preferences

The Amenitiz MCP Server exposes 8 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Amenitiz to LlamaIndex via MCP

Follow these steps to integrate the Amenitiz MCP Server with LlamaIndex.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 8 tools from Amenitiz

Why Use LlamaIndex with the Amenitiz MCP Server

LlamaIndex provides unique advantages when paired with Amenitiz through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Amenitiz tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Amenitiz tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Amenitiz, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Amenitiz tools were called, what data was returned, and how it influenced the final answer

Amenitiz + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Amenitiz MCP Server delivers measurable value.

Hybrid search: combine Amenitiz real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Amenitiz to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Amenitiz for fresh data

Analytical workflows: chain Amenitiz queries with LlamaIndex's data connectors to build multi-source analytical reports

Amenitiz MCP Tools for LlamaIndex (8)

These 8 tools become available when you connect Amenitiz to LlamaIndex via MCP:

get_availability

Get availability

get_property

Get hotel property info

get_rates

Get room rates

get_reservation

Get reservation details

list_guests

List guests

list_reservations

List hotel reservations

list_room_types

List room types

list_rooms

List individual rooms

Example Prompts for Amenitiz in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Amenitiz immediately.

"What rooms are available for next weekend and at what rates?"

Troubleshooting Amenitiz MCP Server with LlamaIndex

Common issues when connecting Amenitiz to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Amenitiz + LlamaIndex FAQ

Common questions about integrating Amenitiz MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Amenitiz tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.