How to Use the Guidebook MCP in LlamaIndex

Index your event data into LlamaIndex for semantic search and grounded answers.

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Connect Guidebook MCP to LlamaIndex

Create your Vinkius account to connect Guidebook 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 Guidebook with LlamaIndex

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Query Guidebook events via LlamaIndex

Convert API results into a searchable knowledge base. By running `list_guides` and indexing the output, you allow your agent to answer questions about event logistics using current data. This prevents the model from hallucinating details about room numbers or session start times. It pulls the facts directly from your live event configuration.

Build RAG apps with Guidebook

Combine your private documents with live API data from `list_locations` and `get_event`. The agent retrieves context from the index to provide precise, grounded responses. This turns static event information into a living repository. Your agent isn't just calling functions; it's reasoning over a unified view of your event ecosystem.

Filter tools for targeted access

Use the allowed_tools filter to restrict the agent to specific operations like `get_guide`. This ensures your RAG application only interacts with the data you authorize. It provides a layer of control over how your agent navigates the server. You define the boundaries, and the agent stays within them while building the index.

Setup guide

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

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Common questions about Guidebook MCP in LlamaIndex

Use the basic client to connect to the server and wrap it in a tool spec. This allows your agent to convert the API output into a queryable format seamlessly.

Absolutely. The tool spec provides async methods for fetching data, ensuring your agent remains responsive while populating the vector index with event details.

Vinkius handles all auth through a single token, isolating your event data from the environment. Your index only contains the information retrieved through approved tool calls.

You can include attendee data in your index for better context, provided you have the right permissions. The server tools handle the data retrieval while your agent manages the indexing process.

It uses strict scope definitions for every tool call. Only the specific session data requested by the agent is ever indexed, keeping your attendee and scheduling information private.

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