AvoSMS MCP Server for LangChain 11 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect AvoSMS through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MultiServerMCPClient({
"avosms": {
"transport": "streamable_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,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using AvoSMS, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About AvoSMS MCP Server
Orchestrate your global mobile communication with AvoSMS, the high-performance messaging platform designed for scale. By connecting AvoSMS to your AI agent, you transform SMS outreach from a manual task into a natural conversation. Your agent can now send instant notifications, schedule future broadcasts, manage approved sender identities, and audit your contact lists without you ever touching a dashboard. Whether you're tracking customer responses or monitoring credit balances, your agent acts as a real-time mobile operations manager for your business.
LangChain's ecosystem of 500+ components combines seamlessly with AvoSMS through native MCP adapters. Connect 11 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
What you can do
- Precision Messaging — Send individual or scheduled SMS messages globally with full support for custom delivery timestamps.
- Identity Control — Request, list, and manage approved Sender IDs (names) to ensure your brand is always recognized.
- Contact Orchestration — Create and manage dedicated contact lists, adding recipients dynamically via natural language.
- Response Auditing — Retrieve and list incoming SMS responses to maintain a two-way dialogue with your audience.
- Account Health — Instantly check your remaining credit balance and verify account connectivity on the fly.
The AvoSMS MCP Server exposes 11 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect AvoSMS to LangChain via MCP
Follow these steps to integrate the AvoSMS MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 11 tools from AvoSMS via MCP
Why Use LangChain with the AvoSMS MCP Server
LangChain provides unique advantages when paired with AvoSMS through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine AvoSMS MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across AvoSMS queries for multi-turn workflows
AvoSMS + LangChain Use Cases
Practical scenarios where LangChain combined with the AvoSMS MCP Server delivers measurable value.
RAG with live data: combine AvoSMS tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query AvoSMS, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain AvoSMS tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every AvoSMS tool call, measure latency, and optimize your agent's performance
AvoSMS MCP Tools for LangChain (11)
These 11 tools become available when you connect AvoSMS to LangChain via MCP:
add_contact
Add a contact to a list
create_list
Create a new contact list
create_sender
Request a new sender ID
delete_list
Delete a contact list
delete_sender
Delete a sender ID
get_account_check
Verify AvoSMS account connection
get_balance
Check remaining SMS credits balance
list_lists
List all contact lists
list_responses
List incoming SMS responses
list_senders
List all approved sender IDs
send_sms
Send an SMS message
Example Prompts for AvoSMS in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with AvoSMS immediately.
"Send an SMS to +33600000000 saying 'Your order is ready for pickup!' using sender 'ShopAlert'."
"Check my AvoSMS credit balance and list recent replies."
"Add the phone number +123456789 to my 'VIP Customers' list."
Troubleshooting AvoSMS MCP Server with LangChain
Common issues when connecting AvoSMS to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersAvoSMS + LangChain FAQ
Common questions about integrating AvoSMS MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
Connect AvoSMS with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect AvoSMS to LangChain
Get your token, paste the configuration, and start using 11 tools in under 2 minutes. No API key management needed.