How to Use the WSLA (WhatsApp) MCP in LangChain

Q: Can I use WSLA (WhatsApp) templates with LangChain?

Absolutely. You can list available templates with listwhatsapptemplates and then instruct your agent to send a message using sendwhatsapptemplate. This is perfect for structured communication.

Q: Does the MCP Server support WSLA (WhatsApp) reactions in LangChain?

Yes. You can add a dedicated step to your chain that calls sendwhatsappreaction after processing data, letting your agent react directly within the conversation flow.

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Connect WSLA (WhatsApp) MCP to LangChain

Create your Vinkius account to connect WSLA (WhatsApp) to LangChain 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 WSLA (WhatsApp) with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Executing simple messages

You can send plain text messages via `send_whatsapp_text`. Your agent uses this tool when it just needs to communicate a quick piece of info on WhatsApp. This is ideal for the final step in a chain. The output from a previous database query, for instance, becomes the direct input for the message body.

Managing structured messages

To send formal or transactional messages, first use `list_whatsapp_templates` to check available options. Then, the agent calls `send_whatsapp_template` to deliver the content using a specific WhatsApp template. This two-step process allows your ReAct agent to validate structure before committing to sending, ensuring compliance with Meta's rules.

Getting media context and reactions

Need to react or get info on attached media? The agent first calls `get_whatsapp_media_details` for necessary context. Later, it uses `send_whatsapp_reaction` to quickly acknowledge the message on WhatsApp. This flow lets you build multi-step logic: check details -> decide action -> perform reaction.

Setup guide

Set up WSLA (WhatsApp) MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes WSLA (WhatsApp) tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "wsla-whatsapp-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent WSLA (WhatsApp) transactions"
    })
    print(result["messages"][-1].content)

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Common questions about WSLA (WhatsApp) MCP in LangChain

Your agent uses LangChain to sequence multiple actions. You can build a chain that first gets media details using `get_whatsapp_media_details`, and then sends a follow-up text via `send_whatsapp_text`.

Absolutely. You can list available templates with `list_whatsapp_templates` and then instruct your agent to send a message using `send_whatsapp_template`. This is perfect for structured communication.

Yes. You can add a dedicated step to your chain that calls `send_whatsapp_reaction` after processing data, letting your agent react directly within the conversation flow.

This MCP Server handles message content (text/templates), media metadata, and user reactions. This is all structured as API call outputs that your chain can consume.

You'll want to use client.session() for persistent context. This lets the agent remember previous WhatsApp interactions and use that history when making future calls.

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