How to Use the 2Chat MCP in LangChain

Build complex WhatsApp messaging chains in LangChain using 2Chat tools.

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Connect 2Chat MCP to LangChain

Create your Vinkius account to connect 2Chat 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 2Chat with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain 2Chat actions in LangChain agents

Connect `send_message` directly to your logic flows. Your agent decides when to trigger a text based on previous chain steps. This MCP server lets you build multi-step reasoning pipelines. You define the sequence where one tool output feeds the next.

Manage WhatsApp groups with LangChain

Automate group creation using `create_group` within your agent graph. It handles participant lists as part of your execution logic. Use `list_groups` to pull existing group IDs into your memory buffers. Your chains reference these IDs to keep messaging accurate.

Verify contact data in LangChain

Run `check_number` before sending data to avoid delivery failures. It plugs right into your validation chain. Use `list_numbers` to identify the correct sender ID for your pipeline. Your agent swaps this ID dynamically based on the current context.

Setup guide

Set up 2Chat 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 2Chat 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({
    "2chat-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 2Chat 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 2Chat. 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 2Chat MCP in LangChain

You need the langchain-mcp-adapters package. Initialize the MultiServerMCPClient with your endpoint, then pass the tools directly to your agent constructor.

Yes. Each tool call acts as a link in your chain. The output from a `list_groups` call becomes the input for your next messaging task.

Vinkius handles authentication with a single token. We never store your WhatsApp messages or group data on our servers.

The framework supports full observability via LangSmith. You can monitor latency, token usage, and specific tool inputs for every message sent.

It is stateless by default. Use the client session method if you need to keep context across multiple agent turns.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.