How to Use the Gallabox MCP in LlamaIndex

Q: How does LlamaIndex index message templates from Gallabox?

LlamaIndex uses listmessagetemplates to pull your approved templates into a vector index. When a user asks a question, the agent performs a semantic search to find and send the most relevant WhatsApp template.

Index live WhatsApp threads into LlamaIndex vector stores to ground your agent in real customer conversations.

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Connect Gallabox MCP to LlamaIndex

Create your Vinkius account to connect Gallabox 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 Gallabox with LlamaIndex

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Index WhatsApp history into LlamaIndex vector stores

The `list_thread_messages` and `list_active_chats` tools allow LlamaIndex to ingest your live WhatsApp history and convert the raw text into searchable document nodes. Your agent queries this indexed conversation history to answer user questions with real context. This prevents your agent from hallucinating details about past customer agreements or support issues. You build RAG pipelines that ground every response in actual chat logs retrieved from the messaging API.

Build WhatsApp RAG pipelines with this MCP Server

The `get_contact_details` and `list_whatsapp_contacts` tools supply the metadata LlamaIndex needs to filter vector searches by specific users. Your query engine targets searches to a single user's thread instead of scanning your entire database. This targeted search keeps your RAG applications fast and accurate. Your agent answers questions about a specific customer's order status by retrieving only that customer's recent WhatsApp messages.

Pull active support team context for LlamaIndex agents

The `list_support_teams` and `get_my_profile` tools let LlamaIndex index your internal team structures with this MCP Server. Your agent queries this index to find out which team is online before dispatching a message. Instead of guessing where to route a customer, the agent matches the query's intent against the indexed capabilities of your active teams. This ensures customers get routed to the right human agent when automated replies aren't enough.

Setup guide

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

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Common questions about Gallabox MCP in LlamaIndex

LlamaIndex uses `list_message_templates` to pull your approved templates into a vector index. When a user asks a question, the agent performs a semantic search to find and send the most relevant WhatsApp template.

Yes, you can combine vector search with metadata filters using contact details retrieved via the MCP Server. This lets you filter chat histories by phone number or name before running semantic queries.

You configure LlamaIndex's node parsers to chunk the message history returned by `list_thread_messages`. This keeps your vector embeddings precise and prevents exceeding LLM context windows.

You initialize the BasicMCPClient with the Vinkius URL and convert it using McpToolSpec. This exposes the entire MCP Server schema to your query engines.

The raw message text stays within your local or cloud vector database configured in LlamaIndex. The server merely acts as a secure, zero-trust pipeline that transfers the data without storing it.

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