How to Use the Vonage MCP in LangChain
Build multi-step communication agents with LangChain.
Works with every AI agent you already use
…and any MCP-compatible client
Connect Vonage MCP to LangChain
Create your Vinkius account to connect Vonage 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.
Send Messages via MCP Server
You can chain a sequence of communications. First, check the available funds by calling `get_account_balance`. Then, use that data to decide if an outgoing message is feasible. After confirming balance, you send the actual communication. For instance, calling `send_sms` lets your agent dispatch an outbound SMS using a specified sender name and E.164 target number.
Manage Identity Verification
The chain handles complex authentication flows automatically. Your agent can start the process by calling `start_verification`, which kicks off sending an OTP code to a target number. If the user submits a code, the next step in the chain calls `check_verification_code`. This validates the input and allows your agent to proceed or abort using `cancel_verification_request`.
List and Manage Numbers
Need to know what numbers are available? The agent uses `list_rented_numbers` to pull a full roster of virtual phone lines assigned to the account. This output can then feed into another step, like running pricing checks with `get_country_pricing`, ensuring your overall workflow knows exactly which geographical codes you're dealing with before sending anything.
Set up Vonage MCP in LangChain
Prerequisites
- Python 3.10+ installed
-
langchain-mcp-adapters+langgraphpackages - 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 LangChainBaseToolobjects. - 2
Connect via HTTP transport
Use
MultiServerMCPClientwith"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 Vonage tool calls through the MCP protocol. - 4
Run with any LLM
Swap
ChatOpenAIforChatAnthropic,ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.
from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI
async with MultiServerMCPClient({
"vonage-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 Vonage transactions"
})
print(result["messages"][-1].content) Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Vonage. 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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Real-time monitoring
Live
visibility into every interaction
Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.
Built-in savings
60%
lower AI costs
Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.
Single dashboard
One
place for every integration
Every tool your AI connects to, managed from a single screen. One account, complete control.
Common questions about Vonage MCP in LangChain
Use it with your favorite AI tools
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