How to Use the Bandwidth MCP in LangChain

Build multi-step messaging chains in LangChain using Bandwidth to automate SMS, MMS, and campaign management in your reasoning pipelines.

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Connect Bandwidth MCP to LangChain

Create your Vinkius account to connect Bandwidth 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 Bandwidth with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain Bandwidth tools for complex messaging

Connect `send_sms` or `send_mms` to your LangChain agents to trigger automated responses based on previous chain outputs. You define the logic that decides when to fire a message, allowing your agent to handle multi-step communication flows without manual intervention. Tracing every call through LangSmith makes debugging these chains simple. You see exactly what inputs fed into the tool and how the messaging API responded, ensuring your logic holds up under load.

Manage campaigns and media within LangChain

Access `list_campaigns` and `get_campaign` to pull 10DLC data directly into your agent state. Your agent can check status or verify toll-free numbers using `get_toll_free` before initiating a send sequence. Handling assets becomes part of your workflow via `list_media` and `delete_media`. The agent cleans up old files or organizes new uploads automatically as part of your execution loop.

Observability for every Bandwidth MCP operation

This MCP Server exposes the full suite of messaging tools to your agent's reasoning process. By integrating these operations into your chains, you get granular control over how your application talks to the Bandwidth platform. Monitoring your agent's behavior is straightforward when you use the provided tool adapters. Each request stays logged, so you catch errors in your messaging logic before they reach your end users.

Setup guide

Set up Bandwidth 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 Bandwidth 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({
    "bandwidth-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 Bandwidth 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 Bandwidth. 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.

Why Choose Vinkius

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place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Bandwidth MCP in LangChain

You use LangSmith to monitor the tool calls. Each Bandwidth operation is logged as a distinct step in your chain, capturing latency and payload data.

Yes. You wrap the tools in error-handling chains. If a message fails, your agent catches the output and attempts a retry or logs the issue.

The server uses a single endpoint token for authentication. Data remains within your runtime environment and isn't stored by the Vinkius infrastructure.

The agent interacts with your 10DLC campaigns, media storage, and message logs. It only touches the resources you explicitly grant permission to use.

You maintain state using the client session object. This keeps your messaging context consistent across multiple chain turns.

Use it with your favorite AI tools

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

OpenAI Agents SDK sdk-python

Google ADK sdk-python