Guance Cloud MCP. Monitor infrastructure health through conversation.
Guance Cloud / 观测云 gives your AI agent full access to complex system monitoring data. Stop clicking through dashboards and log tabs. This MCP lets you ask natural language questions about infrastructure, retrieving monitor statuses, reviewing real-time events, and running deep data queries (DQL) without touching the Guance console.
Give Claude and any AI agent real-world access
The agent retrieves the current metadata and alert status for your entire monitoring workspace.
You can ask the agent to list every configured system monitor and get its detailed setup information.
The agent pulls a stream of real-time observability records, including errors, alerts, and system changes.
You tell the agent what metrics you need, and it executes powerful DQL statements to fetch specific logging or performance data.
The agent provides visibility into your organizational API access keys and current usage billing records.
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What AI agents can do with Guance Cloud / 观测云: 10 Tools for Observability
Use these tools to check system health, list monitors, run DQL queries, and audit billing data through natural conversation.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Guance Cloud / 观测云 MCPGet Billing
Retrieves your current cloud service usage and cost information.
Get Event
Fetches the detailed record for a specific observability event.
Get Monitor
Gets all configuration details for a single system monitor.
Get Workspace
Retrieves high-level metadata and status information about your monitoring workspace.
List Access Keys
Shows a list of all API access keys configured in the workspace for auditing...
List Dashboards
Returns a comprehensive list of every dashboard configured in the system.
List Events
Pulls a list of recent observability events, including alerts and errors.
List Log Sources
Provides an inventory of all available log data sources for analysis.
List Monitors
Lists every active and configured system monitor in the environment.
Query Data
Runs powerful Data Query Language (DQL) statements to fetch specific metrics or log...
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on each call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Guance Cloud / 观测云, then connect any of our 5,200+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,200+ others, all in one place
- Add new capabilities to your AI anytime you want
- Connections are secured and governed automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog weekly
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Guance Cloud / 观测云. 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.
VINKIUS CLOUD
Cloud Hosted
Managed infra
V8 Isolated
Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on each call
GDPR Compliant
EU data residency
Token Compression
~60% cost reduction
The Manual Chore of System Health Checks
Right now, checking if a system is healthy means opening the Guance console. You navigate to the dashboards tab, then you open the monitor list, check status codes one by one, and finally, you have to manually build a query using DQL just to get average CPU usage for a specific time window. It’s clicking through five different tabs just to answer two simple questions.
With this MCP connected via Vinkius, those clicks disappear. You simply talk to your agent: 'Check the status of the high-CPU monitor and give me the 15-minute average.' The agent executes `get_monitor` and `query_data` in the background and spits out a clean answer instantly.
Guance Cloud / 观测云 MCP: Instant Observability Insights
You no longer have to copy-paste an API key into a script or manually reconcile dashboard configurations across different teams. You can ask the agent for `list_access_keys` and instantly get a full, auditable list of every credential in one chat prompt.
Your AI client acts as a dedicated SRE co-pilot, handling all the complex data orchestration—from listing events to running deep DQL queries—so you stay focused on solving the problem, not clicking through the interface.
What Guance Cloud MCP does for your AI
Guance Cloud connects your AI agent directly to an entire observability stack. Forget logging into a dashboard just to check if a service is down or what caused the spike last night. This MCP lets you manage complex system monitoring, log analysis, and incident response simply by talking to your agent.
You can ask it to list all monitors, pull detailed configuration reports on dashboards, or browse specific system events in real time. If you need deep metrics, you don't build a query; you just tell the agent what average CPU usage you want over the last hour. Your agent acts like an always-on site reliability assistant, keeping your infrastructure data accurate and giving you instant answers whether you’re troubleshooting or auditing resource usage.
Connecting Guance Cloud to Vinkius means you get this power alongside thousands of other industry tools through one place.
019d8444-c688-70d0-84d6-0cc533614025 How to set up Guance Cloud MCP
The bottom line is you get instant answers about complex infrastructure metrics by talking to your agent instead of navigating web consoles.
Subscribe to this MCP and provide your Guance Cloud API Key (DF-API-KEY).
Connect the credentials to your preferred AI client, like Cursor or Claude.
Ask your agent a question—for example, 'What was the average latency last night?'—and it handles the data retrieval.
Who uses Guance Cloud MCP
This is for Ops Engineers and SREs who spend too much time clicking through multiple dashboards just to find one metric. If you’re tired of logging in at 2 AM just to figure out why the system failed, this MCP saves your sanity.
You use the agent to automate incident response, asking it to list monitors or check events when a production outage happens.
You ask for specific data using DQL queries and retrieve dashboard configurations without opening the GUI.
You coordinate monitoring strategies by having the agent audit resource usage or check billing details.
Benefits of connecting Guance Cloud MCP
Instead of manually running list_monitors to see what's broken, you simply ask your agent for the status. You get immediate visibility into every alert rule without leaving your chat window.
Need to check past activity? Use the agent to browse real-time observability events via list_events. This means instantly knowing if a system change caused an error, which is way faster than digging through logs.
The biggest time saver: running complex queries. Instead of building DQL in a separate tool and pasting it somewhere else, you just ask the agent to perform data querying using query_data directly.
You don't have to guess what resources exist. Running list_dashboards gives you an immediate overview of your monitoring setup, letting you audit coverage instantly.
It keeps everything connected: You can check the overall status with get_workspace, then drill down to specific alerts using get_monitor. It's a complete workflow built into one conversation.
Guance Cloud MCP use cases
Troubleshooting an unexpected service dip
An SRE gets paged about high latency. Instead of jumping between the metrics dashboard and the event log, they ask their agent to list monitors and then execute a DQL query for average latency across the cluster last night. The agent answers with a clear number and identifies the time window where the issue started.
Auditing resource access
An Infrastructure Lead needs to know which services are using which keys. They ask their agent to list access keys, check billing usage with get_billing, and confirm that all required dashboards are available via list_dashboards. It compiles a full compliance report instantly.
Investigating a data loss incident
A technical analyst suspects log data is missing. They ask the agent to list log sources, then browse recent observability events using list_events to see if any deletion alerts fired. The agent pinpoints the exact time and source of the gap.
Guance Cloud MCP tradeoffs
What to watch out for, and the recommended way to handle each one.
Treating it like a simple search engine
Trying to ask, 'What is my CPU usage?' without specifying the metric or time range. The agent gets confused because you didn't provide enough context.
Always be specific. Instead of a vague query, use query_data and tell your agent: 'Give me the average CPU usage for the Production cluster over the last 15 minutes.' Specificity is key.
Manually copying configuration details
Finding a critical monitor setting in the console, copy-pasting it into a document just to share it with a teammate later.
Use get_monitor to have your agent pull the exact JSON structure and status of that monitor directly into your chat for easy sharing or logging.
Ignoring system metadata
Only looking at error logs without understanding if the underlying workspace itself was configured correctly.
Start by calling get_workspace to get a high-level status overview. This gives you context before diving into specific errors or metrics.
When to use Guance Cloud MCP
Use this MCP when your core task involves monitoring, debugging, or auditing live system health and performance data from Guance Cloud. If you need to know the state of a complex technical system—like 'What happened with my API latency last night?' or 'List all active alerts'—this is perfect.
Don't use it if your goal is simple record creation (you can't create monitors here) or if you only need basic data retrieval that doesn't involve multiple steps, like fetching a single user name. For those simpler tasks, other specialized tools might be better.
If you are analyzing metrics and events, the combination of list_monitors, get_monitor, and query_data is your core workflow.
Frequently asked questions about Guance Cloud MCP
How do I list monitors using Guance Cloud / 观测云 MCP? +
You ask your agent to execute list_monitors. The agent will return a full catalog of every monitor configured in the workspace, letting you know exactly what is being watched.
Can I run custom metrics queries with Guance Cloud / 观测云 MCP? +
Yes. You use the query_data tool. Just tell your agent which metric and time window you want, and it runs the necessary DQL statement for you.
What is the difference between list_events and get_event? +
list_events gives you a feed of recent occurrences (errors, alerts). get_event allows you to deep-dive into one specific event ID to see all its associated details.
Do I need to manually find the API key for Guance Cloud / 观测云 MCP? +
The agent can help. You use list_access_keys to get a list of existing keys and confirm which credentials are available for your workspace.
How do I check billing usage with Guance Cloud / 观测云 MCP? +
You ask the agent about billing, and it executes get_billing. You get an immediate breakdown of your current service consumption and costs.