HyperDX MCP for AI. Centralize observability data into your conversation.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
How this MCP server connects to your AI agent
HyperDX (Open Source Observability) connects your agent directly to your infrastructure data. It lets you search logs, manage alert rules, and inspect dashboards using natural conversation.
Stop jumping between tabs to correlate metrics with error streams; just ask your AI client for the full picture.
What AI agents can do with HyperDX (Open Source Observability) Automation
List alerts
Gets a list of all alert rules currently set up in your organization.
Create alert
Sets up a new alert rule for when specific conditions are met.
List dashboards
Retrieves the names and details for every dashboard available to you.
Retrieve metrics, trends, and visualizations from all available organizational dashboards.
Query massive volumes of structured logs or raw event spans using specific filters for debugging in real-time.
List, create, or delete system alerts to keep track of performance regressions and service health.
Ask an AI about this
Waiting for input…
What AI agents can do with HyperDX (Open Source Observability) MCP with 7 Tools
These tools allow you to list all dashboards, retrieve metrics, search logs and events by specific criteria, and manage alert rules through your AI agent.
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 HyperDX (Open Source Observability) on VinkiusList Alerts
Gets a list of all alert rules currently set up in your organization.
Create Alert
Sets up a new alert rule for when specific conditions are met.
List Dashboards
Retrieves the names and details for every dashboard available to you.
Delete Alert
Removes an existing alert rule using its unique identifier.
List Events
Gathers structured logs or spans from HyperDX using specific query filters.
Get Dashboard
Pulls the detailed metrics for a specific dashboard you name.
List Logs
Retrieves a list of general application logs based on criteria like service name or error level.
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.
Claude AI
Open Claude Settings
Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
Add Custom Connector
Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:
https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp
Replace [YOUR_TOKEN_HERE] with your token
from cloud.vinkius.com. For OAuth-protected servers, expand
Advanced settings to add credentials.
Start a conversation
Open a new chat. The HyperDX integration is available immediately — no restart needed.
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 every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with HyperDX (Open Source Observability), then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,100+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by HyperDX. 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 INFRASTRUCTURE
Cloud Hosted
Managed infra
V8 Isolated
Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on every call
GDPR Compliant
EU data residency
Token Compression
~60% cost reduction
Built on the Model Context Protocol (MCP) for Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 7 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
The pain of cross-referencing logs and alerts manually, Solved with Vinkius AI Gateway
Today, finding the root cause means a painful manual dance. You see an alert pop up in one dashboard, so you switch to another tab to check the time range. Then you copy error IDs into a third tool to search for corresponding logs, often having to manually adjust time filters multiple times just to correlate everything.
With this MCP, that entire process vanishes. Your agent accepts your request—'Check system health after the deploy.' It automatically calls `list_logs` and `list_events`, correlates them by time, and presents the full narrative in one chat response.
Managing monitoring rules with HyperDX
You used to have to navigate a dedicated UI just to see if an alert rule was already set up or to create a new threshold. You'd call `list_alerts` just to check the list, and then maybe use a form to configure a change.
Now you just tell your agent: 'Create an alert for high CPU usage.' It handles the complexity of calling `create_alert`, configuring all the necessary parameters, and confirming the rule is active. Simple.
What your AI can actually do with this
This MCP gives your agent deep visibility into your application performance and system health. You can query structured logs, review raw event data, and check dashboard trends without ever leaving your chat interface. Need to debug an issue? Your agent handles the complex queries—you just ask it for logs from a specific service or time window.
Want to adjust monitoring? You manage alert rules directly through conversation, creating new alerts or deleting old ones. It works by letting you view all available dashboards and getting details on any one of them. Connecting your instance via Vinkius's catalog means you get this powerful observability layer integrated into whatever client you use.
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Here's how it actually works
The bottom line is you talk to it like an on-call engineer talking to a teammate, not like filling out a complex monitoring dashboard.
Subscribe to this MCP in Vinkius and input your HyperDX API Key.
Your AI client sends a request (e.g., 'Show me errors for the auth service').
The system processes the query against HyperDX data and returns the filtered logs, events, or dashboard metrics directly into your chat.
Who is this actually for?
This is for the SRE who gets frustrated having to copy timestamps and pivot between five different dashboards to solve one bug. It’s for the developer stuck in flow when a sudden production issue hits, and it's for the DevOps engineer tired of manually managing alert thresholds.
Manages system alerts by listing existing rules or creating new ones to cover performance gaps.
Quickly checks error rates across services and fetches logs without opening a browser tab.
Retrieves specific structured event data or trace IDs needed to fix production bugs while staying in their coding environment.
What Changes When You Connect
Stop context switching. You can get logs and dashboard metrics in one chat session, eliminating the need to jump between monitoring tools.
Manage alerts directly from your agent using list_alerts or create_alert. Set thresholds for high error rates without touching a UI.
Debugging is faster when you can query structured events via list_events and general logs via list_logs, all filtered by precise time ranges (like 1h or 24h).
Quickly understand system health. Use list_dashboards first, then call get_dashboard to view deep metrics on the specific component you need.
It's an SRE assistant that never sleeps. Your agent handles the complex querying of historical data, so you stay focused on fixing the problem.
See it in action
Investigating a sudden performance drop
A developer notices slow response times. They ask their agent to 'Show me error logs for the checkout service from the last 2 hours.' The agent uses list_logs and immediately points out a spike in timeouts, solving the issue without needing to write complex queries.
Auditing an incident response
An SRE needs to know what happened during last night's deployment. They ask their agent to 'List all events for service X from 1:00 AM to 2:00 AM.' The agent uses list_events and provides the full timeline of structured spans.
Setting up better monitoring
A DevOps engineer wants to prevent future outages. They ask their agent, 'Create an alert if the error count exceeds 50 in the next 15 minutes.' The agent uses create_alert and configures the necessary rule.
Reviewing system health before a call
A team lead needs to prepare for an incident meeting. They ask their agent to 'Show me all dashboards and get details on System Health.' The agent uses list_dashboards followed by get_dashboard, giving the full overview instantly.
The honest tradeoffs
Manual Correlation
Copying a timestamp from an alert notification, switching to the log dashboard, filtering by that time range, then copying error codes into a spreadsheet.
Just ask your agent. Tell it what you need—'Show me logs and events for these errors in the last 24 hours.' The agent handles all the tool calls (list_logs and list_events) automatically.
Tool Overload
Calling list_dashboards, then calling get_dashboard for every single dashboard, one by one.
If you know what metrics matter, ask the agent directly. Instead of listing all dashboards, just say 'Give me details on API Performance' so it uses get_dashboard right away.
Forgetting Time Scope
Asking for logs without specifying a time range, resulting in massive data dumps or incomplete historical views.
Always include the time filter. Use relative terms like 'last 1 hour' or specific ISO dates so it queries with precision.
When It Fits, When It Doesn't
Use this MCP if your primary workflow involves correlating multiple types of data—logs, alerts, and metrics—and you want to keep that entire process conversational. If you spend more time jumping between a log viewer, a dashboarding tool, and an alert manager than you do debugging code, this is for you.
Don't use it if your job is simply to write logs or run single, isolated queries against one specific data source (like just querying user IDs). For those cases, a simple dedicated logging utility might be enough. But when the problem requires synthesizing information from multiple sources, this MCP connects them all.
Questions you might have
How do I find general errors using list_logs? +
Use the agent to call list_logs and include specific query filters. You can filter by level:error or service:auth, for example, to narrow down results immediately.
Can I see all available dashboards using list_dashboards? +
Yes, calling list_dashboards retrieves a complete inventory of every dashboard in the organization. After you get the name, you can use get_dashboard to inspect its metrics.
How do I manage my alerts using create_alert? +
You tell your agent to execute create_alert. You provide parameters like the query and the threshold (e.g., 'errors exceed 50 in 5 minutes'), and it handles setting up the rule.
Is list_events different from list_logs? +
Yes, list_logs retrieves general application logs based on simple search criteria. However, list_events pulls structured events or spans, which are usually more detailed and useful for deep debugging.
What happens if I use `delete_alert` but don't know the alert ID? +
You must supply the unique ID of the rule you want to remove. The system requires this specific identifier because it doesn't support deleting alerts by name or pattern.
How do I use `get_dashboard` if I only know the dashboard's purpose, not its ID? +
First, run list_dashboards to get a list of IDs. Then, pass the specific ID you need to get_dashboard. This allows your agent to retrieve detailed metrics for that single board.
Can I filter logs using relative time ranges when running `list_logs`? +
Yes, you can use relative parameters like '1h' or '24h'. This is ideal for troubleshooting because it lets your agent query data based on a duration without needing specific ISO 8601 timestamps.
Is there a way to see the full configuration of an event stream using `list_events`? +
The tool retrieves structured events and spans. If you need details beyond just the list, you'll use another function that accepts a specific dashboard ID for deep inspection.
Can I search for specific errors in my logs using this server? +
Yes! Use the list_logs tool with a query like level:error. You can also specify a time range using the from parameter (e.g., '1h' or '24h') to narrow down the results.
How do I set up a new alert for a specific service? +
You can use the create_alert tool. You'll need to provide a name, the search query (e.g., service:auth level:error), a threshold value, the type of alert (like 'count'), and the evaluation interval (e.g., '5m').
Is it possible to delete an alert rule if it's no longer needed? +
Yes, simply use the delete_alert tool and provide the unique ID of the alert rule you wish to remove.
We've already built the connector for HyperDX. Just plug in your AI agents and start using Vinkius.
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