Tingyun / 听云 MCP Server for LangChain 10 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Tingyun / 听云 through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
ASK AI ABOUT THIS MCP SERVER
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({
"tingyun": {
"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 Tingyun / 听云, 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 Tingyun / 听云 MCP Server
Empower your AI agent to orchestrate your entire digital performance stack with Tingyun (听云), the premier APM and observability platform. By connecting Tingyun to your agent, you transform complex application monitoring, incident response, and performance auditing into a natural conversation. Your agent can instantly list monitored applications, retrieve real-time performance summaries, browse active alerts, and query specific metric data without you ever needing to navigate the Tingyun console. Whether you are troubleshooting a production bottleneck or auditing system health across a distributed architecture, your agent acts as a real-time site reliability assistant, keeping your performance data accurate and your systems responsive.
LangChain's ecosystem of 500+ components combines seamlessly with Tingyun / 听云 through native MCP adapters. Connect 10 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
- Application Orchestration — List all APM applications and retrieve detailed health and performance summaries.
- Incident Control — Monitor active alerts and browse alert policies to identify and respond to performance issues.
- Infrastructure Auditing — List application instances, external service calls, and database dependencies.
- Metric Querying — Retrieve specific metric data points for applications to analyze trends and anomalies.
- User Experience Insights — Browse Real User Monitoring (RUM) browser applications to audit frontend performance.
The Tingyun / 听云 MCP Server exposes 10 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 Tingyun / 听云 to LangChain via MCP
Follow these steps to integrate the Tingyun / 听云 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 10 tools from Tingyun / 听云 via MCP
Why Use LangChain with the Tingyun / 听云 MCP Server
LangChain provides unique advantages when paired with Tingyun / 听云 through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Tingyun / 听云 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 Tingyun / 听云 queries for multi-turn workflows
Tingyun / 听云 + LangChain Use Cases
Practical scenarios where LangChain combined with the Tingyun / 听云 MCP Server delivers measurable value.
RAG with live data: combine Tingyun / 听云 tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Tingyun / 听云, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Tingyun / 听云 tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Tingyun / 听云 tool call, measure latency, and optimize your agent's performance
Tingyun / 听云 MCP Tools for LangChain (10)
These 10 tools become available when you connect Tingyun / 听云 to LangChain via MCP:
get_account_info
Get account metadata
get_app_summary
Get application summary
get_metrics
Query metric data
list_alert_policies
List alert policies
list_alerts
List active alerts
list_app_instances
List application instances
list_applications
List APM applications
list_browser_apps
List RUM browser applications
list_databases
List monitored databases
list_external_services
List external service calls
Example Prompts for Tingyun / 听云 in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Tingyun / 听云 immediately.
"List all applications monitored by Tingyun."
"Show me the performance summary for application ID 12345."
"Check for any critical alerts in Tingyun from today."
Troubleshooting Tingyun / 听云 MCP Server with LangChain
Common issues when connecting Tingyun / 听云 to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersTingyun / 听云 + LangChain FAQ
Common questions about integrating Tingyun / 听云 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 Tingyun / 听云 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 Tingyun / 听云 to LangChain
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.