SignalWire MCP Server for LangChain 8 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect SignalWire 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({
"signalwire": {
"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 SignalWire, 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 SignalWire MCP Server
Empower your AI agent to orchestrate your entire cloud communication infrastructure with SignalWire, the advanced platform for messaging, voice, and video. By connecting SignalWire to your agent, you transform complex telecom management into a natural conversation. Your agent can instantly list your phone numbers, audit message delivery, and retrieve call logs without you ever touching a technical console. Whether you are providing customer alerts or managing corporate voice lines, your agent acts as a real-time telecom operator, ensuring your communication is always reliable and your usage data is organized.
LangChain's ecosystem of 500+ components combines seamlessly with SignalWire through native MCP adapters. Connect 8 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
- Messaging Intelligence — Send SMS messages instantly and retrieve detailed message status and history.
- Call Auditing — List all recent voice calls and retrieve metadata for each, including direction and duration.
- Number Oversight — List and monitor all incoming phone numbers associated with your project.
- Usage Intelligence — Retrieve detailed usage records to maintain strict organizational control over your communication costs.
- Account Governance — Monitor account-wide metadata to understand your project status in real-time.
The SignalWire MCP Server exposes 8 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 SignalWire to LangChain via MCP
Follow these steps to integrate the SignalWire 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 8 tools from SignalWire via MCP
Why Use LangChain with the SignalWire MCP Server
LangChain provides unique advantages when paired with SignalWire through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine SignalWire 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 SignalWire queries for multi-turn workflows
SignalWire + LangChain Use Cases
Practical scenarios where LangChain combined with the SignalWire MCP Server delivers measurable value.
RAG with live data: combine SignalWire tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query SignalWire, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain SignalWire tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every SignalWire tool call, measure latency, and optimize your agent's performance
SignalWire MCP Tools for LangChain (8)
These 8 tools become available when you connect SignalWire to LangChain via MCP:
get_account_info
Get SignalWire account details
get_call
Get details for a specific call
get_message
Get details for a specific message
list_calls
List recent voice calls
list_messages
List recent SMS/MMS messages
list_phone_numbers
List SignalWire phone numbers
list_usage
Get account usage records
send_sms
Send an SMS message
Example Prompts for SignalWire in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with SignalWire immediately.
"List all my SignalWire phone numbers."
"Send SMS 'Server alert: high usage detected' to +15550123."
"Show me recent call logs for my project."
Troubleshooting SignalWire MCP Server with LangChain
Common issues when connecting SignalWire to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersSignalWire + LangChain FAQ
Common questions about integrating SignalWire 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 SignalWire 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 SignalWire to LangChain
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.