How to Use the BulkSMS MCP in LangChain

Q: How do I check my BulkSMS credits in LangChain?

Simply invoke the getprofile tool within your chain. The agent reads the response and updates its state, allowing it to act only when you have enough credits available.

Build chains that trigger BulkSMS actions directly in LangChain pipelines to automate your messaging flow.

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Connect BulkSMS MCP to LangChain

Create your Vinkius account to connect BulkSMS 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 BulkSMS with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain SMS actions in LangChain

Feed the output of your reasoning chain directly into `send_bulk_sms`. Your agent decides exactly when to trigger a message based on previous steps in your workflow. Since LangChain tracks every step, you see the exact input passed to `get_message` before the tool executes. This keeps your messaging logic transparent and debuggable.

Automate contact management

Use `list_groups` and `list_group_members` to pull distribution lists into your agent's context. Your chains can then iterate through these members to personalize your outreach at scale. This MCP Server allows your agents to handle data dependencies without writing custom glue code. You just map the tool outputs to your next prompt.

Monitor credits and status

Call `get_profile` at the start of any chain to check your credit balance. If the balance is low, your agent can pause the sequence before attempting to send messages. Use `list_messages` to verify delivery outcomes for your recent sends. Your agent can then decide if a follow-up is necessary based on the returned delivery status.

Setup guide

Set up BulkSMS 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 BulkSMS 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({
    "bulksms-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 BulkSMS 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 BulkSMS. 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 BulkSMS MCP in LangChain

You pass the tools from this MCP Server into your agent definition using the standard tool-calling interface. Once configured, your agent invokes `send_sms` or `send_bulk_sms` as part of its natural reasoning process.

Yes, you can define a loop or a sequence in LangChain that processes your list of recipients. The agent calls the relevant BulkSMS tool for each batch until the task is complete.

Absolutely, as every interaction with the BulkSMS tools is logged as a tool call in your LangSmith traces. You get full visibility into the arguments sent to the server and the resulting message IDs.

Simply invoke the `get_profile` tool within your chain. The agent reads the response and updates its state, allowing it to act only when you have enough credits available.

Your SMS logs, phone numbers, and account credentials remain within your controlled execution environment. The server only transmits the specific data required to fulfill the requested tool operation.

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