How to Use the Wati MCP in LlamaIndex

Q: How does LlamaIndex use Wati data?

LlamaIndex takes the output from any MCP tool call—like listwhatsappcontacts or sendtemplatemessage results—and indexes it into a vector store. This makes the API data searchable for semantic context.

Augment your knowledge base with LlamaIndex for semantic search over Wati API data.

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Connect Wati MCP to LlamaIndex

Create your Vinkius account to connect Wati 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 Wati with LlamaIndex

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Query WhatsApp chat history using MCP Server

LlamaIndex indexes the results from `get_whatsapp_chat_history` into your vector store. This means you don't just retrieve a log; you can query for semantic answers like, 'What was discussed about pricing last month?' The system grounds answers in actual message data, preventing hallucinations and providing actionable context.

Search Wati contacts and templates with LlamaIndex

When you call `list_whatsapp_contacts` or `list_whatsapp_templates`, the output becomes searchable knowledge. You can ask complex questions like, 'Which template should we use for a contact in Texas?' The index combines this live API data with your other documents, creating a unified queryable source.

Understand broadcast campaigns via LlamaIndex

You can feed the results of `list_broadcast_campaigns` into your RAG pipeline. This lets you build applications that answer questions about campaign performance or content without needing to write complex filtering logic. The result isn't just a list; it’s a searchable record of past efforts.

Setup guide

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

LlamaIndex takes the output from any MCP tool call—like `list_whatsapp_contacts` or `send_template_message` results—and indexes it into a vector store. This makes the API data searchable for semantic context.

You can query past configurations, message history, or contact details using natural language. For example: 'Find all contacts we spoke to about billing last quarter.'

Absolutely. You can pass your private corporate documents alongside the API results from Wati, allowing the agent to synthesize answers using both sources.

It provides the raw message logs. By indexing this data, your AI client can answer contextual questions about past conversations rather than just listing timestamps.

The server touches contact phone numbers and message history. Since the tool output is indexed into a searchable knowledge base, access control over that index is critical for maintaining privacy.

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