How to Use the HQBeds MCP in LlamaIndex

Q: How do I load HQBeds tools into LlamaIndex?

Install the llama-index-tools-mcp package. You instantiate a BasicMCPClient, wrap it in an McpToolSpec, and call totoollistasync() to feed the operations into your FunctionAgent.

Q: Can LlamaIndex search through my past HQBeds reservations?

Yes. By calling listreservations, you can ingest your booking history into a vector store. This allows your agent to perform semantic searches across past occupancy data.

Q: How do I restrict which HQBeds MCP tools the agent can use?

You can pass an allowedtools filter when configuring the MCP tool spec. This lets you expose read-only tools like room lists while hiding write operations like booking creation.

Index HQBeds MCP Server data directly into LlamaIndex to build RAG applications that query live hostel availability and guest ledgers.

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Connect HQBeds MCP to LlamaIndex

Create your Vinkius account to connect HQBeds 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 HQBeds with LlamaIndex

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Query live hostel capacity via LlamaIndex

The `list_availability` tool pulls real-time open bed counts into your LlamaIndex workflow. Instead of guessing if a dorm is full, your application fetches the exact ISO 8601 date window and indexes the result. You ask your agent a question, and it checks the live API data before answering. You combine this with `list_rooms` to build a complete semantic map of your property. The agent reads the physical room layouts and cross-references them against the availability index. When a user asks about group bookings, they get an answer grounded in actual database reality, not a hallucination.

Index guest and booking ledgers

The `list_guests` and `list_reservations` tools feed your vector store with current occupancy records. Your RAG application ingests the daily arrival and departure lists. When front desk staff search for a specific booking trend, the agent retrieves the exact historical context. For deeper context, the agent calls `get_guest` and `get_reservation`. It pulls the specific details of a single traveler's stay and adds it to the working memory. You build a system that knows exactly who is in the building and what they paid, all searchable through natural language.

Execute backend operations

The `create_reservation` tool turns your LlamaIndex setup from a read-only search engine into an active booking agent. Once the user confirms the dates and room types through a semantic search query, the agent constructs the final payload and pushes it to the server. You manage the system health by calling `check_hqbeds_status` and `get_account`. The application verifies the connection and account limits before attempting to process bulk operations. You get a reliable, grounded pipeline that handles both knowledge retrieval and data entry.

Setup guide

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by HQBeds. 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.

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Common questions about HQBeds MCP in LlamaIndex

Install the `llama-index-tools-mcp` package. You instantiate a `BasicMCPClient`, wrap it in an `McpToolSpec`, and call `to_tool_list_async()` to feed the operations into your `FunctionAgent`.

Yes. By calling `list_reservations`, you can ingest your booking history into a vector store. This allows your agent to perform semantic searches across past occupancy data.

The availability endpoints demand ISO 8601 formatting. Your agent must format dates correctly before calling the tools, otherwise the API will reject the request.

You can pass an `allowed_tools` filter when configuring the MCP tool spec. This lets you expose read-only tools like room lists while hiding write operations like booking creation.

The server processes raw guest identities and room assignments. Vinkius runs these operations inside a secure, ephemeral environment. Your single endpoint token authenticates the request, and the connection drops the moment the data transfers to your local index.

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