How to Use the Bookeo MCP in LlamaIndex

Index your Bookeo business data into LlamaIndex for grounded, semantic search across all your bookings.

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Connect Bookeo MCP to LlamaIndex

Create your Vinkius account to connect Bookeo 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 Bookeo with LlamaIndex

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Query Bookeo data through LlamaIndex

Convert `list_products` output into a vector index for semantic retrieval. You can ask your agent about specific service durations and capacities without writing custom search queries. This turns your static booking data into a searchable knowledge base. The agent finds relevant products by matching your intent against the indexed descriptions.

Grounded booking answers

Use `list_bookings` to populate a context window for your RAG pipeline. The agent retrieves the latest reservation status before answering questions, ensuring it never hallucinates about availability. This forces the agent to rely on live data. If a slot isn't in the returned list, the agent knows it's unavailable.

Persistent knowledge stores

Store `get_business_info` results in your vector store to keep business hours and policy details readily available. The agent queries this index whenever a customer asks about your operations. This keeps your agent informed. You update the business data, and the index refreshes to reflect your current reality.

Setup guide

Set up Bookeo 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 Bookeo 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 Bookeo tools.",
)
response = await agent.run("List recent Bookeo 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 Bookeo. 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 Bookeo MCP in LlamaIndex

You can configure your pipeline to ingest booking logs into a vector store. This lets the agent perform semantic searches across past customer interactions and reservations.

Refresh your vector store by triggering the tools periodically via your pipeline. This keeps your search results aligned with the latest data in your account.

It can, provided you index the product data returned by the server. Once indexed, the agent maps user questions to the specific price points defined in your account.

It is, as the tool output format is standard JSON. You pass this directly into your ingestion pipeline to populate your storage context.

Data is encrypted in transit and only processed within your local vector index. No raw payment information is cached on our servers, ensuring your revenue details stay under your control.

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