How to Use the Avochato MCP in LangChain

Q: How do I send a message using Avochato and LangChain?

Your agent uses the sendmessage tool. You can chain it with other tools, like using listcontacts first to find the recipient's phone number, and then passing that to the message tool.

Q: Can a LangChain agent create new Avochato contacts?

Yes, it uses the createcontact tool. A common pattern is to have the agent first check if a contact exists and only create one if it doesn't, preventing duplicates.

Q: What's the best way to manage Avochato tickets using LangChain?

Create a chain. The agent can listtickets to find open issues, use updateticket to change the status, and then sendmessage to inform the customer about the update.

Build agents that manage Avochato contacts and conversations step-by-step with LangChain chains.

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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

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Chain Avochato Actions Together

Your LangChain agent can now run sequences of operations in Avochato. It can `list_contacts` to find the right person, `get_contact` to check their details, and then `send_message` with the correct information, all in one logical flow. This is more than just single API calls. You build chains of reasoning. An agent can `list_tickets`, check a ticket's status, decide to close it with `update_ticket`, and then automatically notify the customer with `send_message`. LangSmith gives you a full trace of every step.

Your LangChain MCP Server for Support

Turn support workflows into autonomous agents. A LangChain agent can monitor for new customers, check if they exist in Avochato using `list_contacts`, and if not, add them with `create_contact` before sending a welcome text. It's also built for managing existing conversations. Your agent can pull up a customer's entire support history by combining `list_tickets` and `list_messages`. This gives it the context needed to make a smart decision, not just a scripted one.

Configure Avochato with Agents

Stop configuring things by hand. Your agent can handle the setup. Give it a task to set up a new notification endpoint, and it will use `list_webhooks` to see what's there, then `create_webhook` to add the new one. This is perfect for automating your environment setup. A single LangChain script can use this MCP Server to get account details with `get_account_info` and configure all necessary webhooks without you ever opening the UI. It's repeatable and auditable.

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-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 Avochato 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 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

Your agent uses the `send_message` tool. You can chain it with other tools, like using `list_contacts` first to find the recipient's phone number, and then passing that to the message tool.

Yes, it uses the `create_contact` tool. A common pattern is to have the agent first check if a contact exists and only create one if it doesn't, preventing duplicates.

Create a chain. The agent can `list_tickets` to find open issues, use `update_ticket` to change the status, and then `send_message` to inform the customer about the update.

LangChain agents can be designed with error handling logic. If a call to `send_message` fails, for example, you can build the chain to retry the action or call a different tool to log the error.

Data like contact details and message content is passed through the agent's chain during execution. The Vinkius server is ephemeral and doesn't store your data, and you control the visibility and persistence of tool inputs and outputs in your LangSmith traces.

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