HQBeds MCP Server for LlamaIndexGive LlamaIndex instant access to 10 tools to Check Hqbeds Status, Create Reservation, Get Account, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add HQBeds 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 App Connector for LlamaIndex
The HQBeds app connector for LlamaIndex is a standout in the Erp Operations category — giving your AI agent 10 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 HQBeds. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in HQBeds?"
)
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 HQBeds MCP Server
Connect your HQBeds account to any AI agent and take full control of your property management system (PMS) and automated hostel/hotel operations through natural conversation.
LlamaIndex agents combine HQBeds tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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
- Reservation Portfolio Orchestration — List and manage all property reservations programmatically, retrieving detailed stay metadata and payment statuses
- Guest & Customer Intelligence — Programmatically retrieve directories of guests and access complete profiles and check-in history in real-time
- Room & Inventory Architecture — Access your complete directory of rooms and availability to coordinate your organizational resource allocation
- Operational Monitoring — Access real-time status updates for check-ins/outs and track property performance directly through your agent for instant reporting
- Infrastructure Verification — Verify account-level API connectivity and monitor booking volume directly through your agent for perfectly coordinated service scaling
The HQBeds MCP Server exposes 10 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.
All 10 HQBeds tools available for LlamaIndex
When LlamaIndex connects to HQBeds through Vinkius, your AI agent gets direct access to every tool listed below — spanning reservation-management, hostel-management, occupancy-tracking, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Verify HQBeds API connectivity
Create a reservation
Get account info
Get guest details
Get reservation details
Get room details
Use ISO 8601 dates. Check room availability
List all guests
List all reservations
List all rooms
Connect HQBeds to LlamaIndex via MCP
Follow these steps to wire HQBeds into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the HQBeds MCP Server
LlamaIndex provides unique advantages when paired with HQBeds through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine HQBeds tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain HQBeds tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query HQBeds, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what HQBeds tools were called, what data was returned, and how it influenced the final answer
HQBeds + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the HQBeds MCP Server delivers measurable value.
Hybrid search: combine HQBeds real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query HQBeds 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 HQBeds for fresh data
Analytical workflows: chain HQBeds queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for HQBeds in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with HQBeds immediately.
"List all reservations checking in today."
"Show room availability for this weekend."
"Create a reservation for Maria Silva, Room 205, checking in May 10 and out May 12."
Troubleshooting HQBeds MCP Server with LlamaIndex
Common issues when connecting HQBeds to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpHQBeds + LlamaIndex FAQ
Common questions about integrating HQBeds MCP Server with LlamaIndex.