How to Use the Wati MCP in LangChain

Q: How do I check the status of Wati when building chains with LangChain?

You simply call the checkwatistatus tool. The chain accepts this output—it's a direct boolean or status message—and uses it to decide whether proceeding with other steps, like sending messages, is safe.

Q: Can I get contact details and then send a message using LangChain?

Yes. The chain first runs getcontact to pull the necessary ID or attributes. It takes that retrieved data and passes it directly to tools like sendsessionmessage, ensuring the recipient is correctly addressed.

Q: Does Wati's MCP Server handle media in LangChain?

Yep. You can use sendmediamessage within a chain. The agent determines the necessary inputs (the media payload and the contact ID) and executes the call, making it part of your multi-step workflow.

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Setup Wati with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Manage Contacts Step-by-Step

Use the `get_contact` tool to pull specific details on a user. You can then call `update_contact` immediately after to change an attribute, like adding a new tag or correcting a phone number. This sequential flow lets your agent verify data before acting. For example, it pulls contact info, checks the status via `check_wati_status`, and then uses the result of that check to decide if sending a message is possible.

Execute Multi-Part Messaging Flows

Start by listing available templates using `list_templates` to see what messaging options exist. Next, your chain can select the correct template and execute it with `send_template_message`. You don't have to stop there; you can then use `list_broadcasts` to confirm if the message was successfully distributed. This mechanism builds complex reasoning paths. The agent determines the optimal tool sequence—like listing contacts, getting a specific ID, and finally sending the targeted message.

Gather Comprehensive Message History

When an agent needs context, it can call `list_messages` to retrieve past communication history. This output serves as the input for subsequent steps in your chain. You'll get a full record of messages exchanged, which is vital for decision-making. Furthermore, you can list tags with `list_tags` and then cross-reference those tags against contacts using `list_contacts`. The entire process remains traceable within LangSmith.

Setup guide

Set up Wati 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 Wati 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({
    "wati-alternative-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 Wati transactions"
    })
    print(result["messages"][-1].content)

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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 LangChain

You simply call the `check_wati_status` tool. The chain accepts this output—it's a direct boolean or status message—and uses it to decide whether proceeding with other steps, like sending messages, is safe.

Yes. The chain first runs `get_contact` to pull the necessary ID or attributes. It takes that retrieved data and passes it directly to tools like `send_session_message`, ensuring the recipient is correctly addressed.

Yep. You can use `send_media_message` within a chain. The agent determines the necessary inputs (the media payload and the contact ID) and executes the call, making it part of your multi-step workflow.

The server exposes various structured data, including full message content from `list_messages`, contact attributes from `get_contact`, and the specific structure of communication templates via `list_templates`.

Since this is an MCP Server connection, changes made by calling `update_contact` are directed to Wati's system. The server handles the persistence of the updated contact attributes.

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