How to Use the Avochato MCP in LangChain

Q: Can my LangChain agent search past Avochato conversations?

Yes, the agent uses listmessages to fetch historical texts. It parses the thread to find previous customer requests before sending a new reply.

Q: How do I verify that my API credentials are correct?

Use the getaccountcheck tool to verify your connection instantly. The agent runs this check at the start of a run to prevent failed runs.

Run Avochato SMS campaigns and manage contacts directly inside your LangChain agentic workflows.

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Connect Avochato MCP to LangChain

Create your Vinkius account to connect Avochato 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 Avochato with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Run multi-step SMS sequences with LangChain

This MCP integration lets the `send_message` tool send text messages to your customers while your agent tracks the conversation history. When a customer replies, your agent uses `list_messages` to read the context and decide the next action without human intervention. You feed these outputs directly into the next link of your chain. LangSmith tracks the latency of every message sent, letting you debug exactly where a conversation stalled.

Sync customer contacts across databases

The `create_contact` tool registers new phone numbers in your database the moment they text in. If a customer changes their details, the agent triggers `update_contact` to keep your CRM accurate. This setup turns your agent into an active dispatcher. It uses the MCP Server to search existing records using `list_contacts` before creating duplicates, keeping your contact database clean.

Broadcast updates to targeted lists using this MCP Server

The `create_broadcast` tool schedules messages to entire groups of customers simultaneously. Your agent pulls the recipient list from a database, formats the payload, and triggers the broadcast. To keep things running smoothly, `list_broadcasts` checks the status of pending campaigns. You get full visibility into what went out and when, all managed by your autonomous pipelines.

Setup guide

Set up Avochato 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 Avochato 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({
    "avochato-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 Avochato transactions"
    })
    print(result["messages"][-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Avochato. 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 Avochato MCP in LangChain

Install the adapter package and initialize the MCP client with your Vinkius endpoint. Call `client.get_tools()` to retrieve the operations, then pass them straight to your agent constructor.

Yes, the agent uses `list_messages` to fetch historical texts. It parses the thread to find previous customer requests before sending a new reply.

Absolutely. Your chain pulls a list of phone numbers, then uses `create_broadcast` to dispatch the group message in a single step.

Use the `get_account_check` tool to verify your connection instantly. The agent runs this check at the start of a run to prevent failed runs.

Your SMS transcripts and contact details pass through the Vinkius secure V8 sandbox. No message data is stored on our servers, keeping your customer phone numbers and text history private.

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