Tingyun / 听云 MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Tingyun / 听云 as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Tingyun / 听云. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Tingyun / 听云?"
)
print(response)
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.
LlamaIndex agents combine Tingyun / 听云 tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Tingyun / 听云 MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from Tingyun / 听云
Why Use LlamaIndex with the Tingyun / 听云 MCP Server
LlamaIndex provides unique advantages when paired with Tingyun / 听云 through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Tingyun / 听云 tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Tingyun / 听云 tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Tingyun / 听云, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Tingyun / 听云 tools were called, what data was returned, and how it influenced the final answer
Tingyun / 听云 + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Tingyun / 听云 MCP Server delivers measurable value.
Hybrid search: combine Tingyun / 听云 real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Tingyun / 听云 to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Tingyun / 听云 for fresh data
Analytical workflows: chain Tingyun / 听云 queries with LlamaIndex's data connectors to build multi-source analytical reports
Tingyun / 听云 MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Tingyun / 听云 to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Tingyun / 听云 to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpTingyun / 听云 + LlamaIndex FAQ
Common questions about integrating Tingyun / 听云 MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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 LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.