Mews MCP Server for LlamaIndex 12 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Mews as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
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 Mews. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in Mews?"
)
print(response)
asyncio.run(main())
* 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 Mews MCP Server
Connect your Mews hotel to any AI agent and transform your front desk into an intelligent, voice-ready concierge.
LlamaIndex agents combine Mews tool responses with indexed documents for comprehensive, grounded answers. Connect 12 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 — Today's check-ins, upcoming arrivals, room assignments, and booking status
- Guests — Search profiles, loyalty tiers, preferences, allergies, and past stays
- Rooms — Real-time room status: vacant/occupied, housekeeping (clean/dirty/inspected)
- Billing — Guest folios, charges, payments, and balance tracking
- Rates & Services — Rate plans, bookable services, and POS outlet items
- Property — Hotel configuration, departments, and operational settings
The Mews MCP Server exposes 12 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 Mews to LlamaIndex via MCP
Follow these steps to integrate the Mews 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 12 tools from Mews
Why Use LlamaIndex with the Mews MCP Server
LlamaIndex provides unique advantages when paired with Mews through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Mews tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Mews tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Mews, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Mews tools were called, what data was returned, and how it influenced the final answer
Mews + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Mews MCP Server delivers measurable value.
Hybrid search: combine Mews real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Mews 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 Mews for fresh data
Analytical workflows: chain Mews queries with LlamaIndex's data connectors to build multi-source analytical reports
Mews MCP Tools for LlamaIndex (12)
These 12 tools become available when you connect Mews to LlamaIndex via MCP:
get_guest
Get guest profile
get_property_info
Get hotel configuration
get_reservation
Get reservation details
get_reservations
Filter by date range. THE core tool — "Who is checking in today?" "How many rooms tonight?" Get hotel reservations
list_bills
Filter by guest for their complete financial history. List guest bills
list_outlet_items
With pricing and categories. List POS items
list_payments
With amounts, dates, and settlement status. List payments
list_rates
With pricing, restrictions, and availability rules. List room rates
list_room_blocks
With dates, room types, and release dates. List room blocks
list_rooms
List hotel rooms
list_services
List hotel services
search_guests
Returns profile, contact info, nationality, loyalty status, past stays, preferences, and billing history. Search hotel guests
Example Prompts for Mews in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Mews immediately.
"Who is checking in today and are there any VIP guests?"
"Show me the housekeeping status for the 3rd floor."
"What is our average daily rate (ADR) for this weekend?"
Troubleshooting Mews MCP Server with LlamaIndex
Common issues when connecting Mews to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpMews + LlamaIndex FAQ
Common questions about integrating Mews MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Mews with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Mews to LlamaIndex
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.