How to Use the AvoSMS MCP in LangChain

Q: How do I connect AvoSMS to my LangChain agent?

Install langchain-mcp-adapters. Pass your Vinkius HTTP endpoint to MultiServerMCPClient and hand the resulting tools to your ReAct agent.

Q: Can LangChain read incoming AvoSMS replies?

Yes. The listresponses tool pulls recent inbound messages. Your chain parses these replies and triggers follow-up actions automatically.

Q: What happens if my AvoSMS balance is zero?

The API rejects the send request. Smart agents use getbalance first to verify credits before attempting to push messages through the MCP Server.

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Setup AvoSMS with LangChain

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AvoSMS MCP Server Pipelines

`send_sms` and `list_responses` connect directly into your ReAct agents. Your LangChain setup decides when to text a user based on previous chain outputs. If a database query fails, the agent immediately fires off an alert text through the MCP Server. You get full observability through LangSmith. Every `add_contact` or `create_list` call logs token usage and latency. You see exactly why the agent picked a specific contact list before hitting the API.

Balance and Sender Management

`get_balance` acts as a programmatic gate in your workflows. Before launching a massive campaign, your agent checks the credit limit. If funds run low, the chain halts and alerts you instead of failing silently mid-send. Managing identities happens entirely in code. `create_sender` requests new IDs, while `list_senders` pulls approved ones into your prompt context. The LLM picks the right caller ID for the target demographic automatically.

Dynamic Contact Lists

`create_list` builds target segments on the fly. When your application processes a new user signup, the agent grabs their details and pushes them straight into AvoSMS. You skip manual CSV uploads entirely. Cleaning up is just as direct. `delete_list` wipes out temporary campaign groups once the event ends. Your agent handles the entire lifecycle from creation to teardown without human intervention.

Setup guide

Set up AvoSMS 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 AvoSMS 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({
    "avosms-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 AvoSMS 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 AvoSMS. 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 AvoSMS MCP in LangChain

Install `langchain-mcp-adapters`. Pass your Vinkius HTTP endpoint to `MultiServerMCPClient` and hand the resulting tools to your ReAct agent.

Yes. The `list_responses` tool pulls recent inbound messages. Your chain parses these replies and triggers follow-up actions automatically.

Every tool call registers in LangSmith. You track exactly what text your agent sent via `send_sms` and measure the API latency.

The API rejects the send request. Smart agents use `get_balance` first to verify credits before attempting to push messages through the MCP Server.

Vinkius runs the AvoSMS integration in an ephemeral V8 Isolate Sandbox. Phone numbers, sender IDs, and message contents vanish from memory the moment the request finishes. Nothing persists on our end.

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