Airbrake MCP for AI. See real-time errors and deployment health status.
Works with every AI agent you already use
…and any MCP-compatible client








Connect to your AI in seconds.
Airbrake provides proactive monitoring for your codebase health and application performance. This MCP lets your AI client automatically track error spikes in real time, analyze specific failure groups, and correlate current errors against deployment versions.
Check API connectivity, review all environments, or report a custom bug notice—all through natural conversation.
What your AI can do
Check airbrake status
Verifies that the MCP has active and functional API connectivity to Airbrake.
Get error group
Retrieves detailed information about a specific group of error types, including full stack traces.
Get project
Fetches all configuration details for one specific monitored project.
Get a list of all monitored applications and pull detailed configuration data for any specific project ID.
View groups of similar errors, see which ones are most frequent, check their severity, or inspect the full stack trace for a single instance.
Track all past releases (Installments) and record new version deployments, linking them to specific environments like production or staging.
Verify that the API connection is active and your Airbrake account is operational. This confirms you can actually send data.
Submit a custom, immediate report of an error when automated tracking isn't possible or necessary.
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Airbrake: 10 Monitoring Tools
These tools allow your agent to perform every function necessary for system observability—from checking basic API status to analyzing deep stack traces.
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 Airbrake on VinkiusCheck Airbrake Status
Verifies that the MCP has active and functional API connectivity to Airbrake.
Get Error Group
Retrieves detailed information about a specific group of error types, including full...
Get Project
Fetches all configuration details for one specific monitored project.
List Deploys
Generates a list of all past and current application deployments (Installments).
List Environments
Shows every configured environment, like 'staging' or 'production', for the...
List Error Groups
Lists available error groups for a project, providing occurrence counts and severity levels.
List Notices
Retrieves a list of individual reported error notices that occurred over time.
List Projects
Returns a comprehensive list of every application or service monitored by Airbrake.
Report Notice
Allows the user to submit a new, custom-defined error notice for tracking purposes.
Track Deploy
Records a new deployment version and environment name against a specific project ID.
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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
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- 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 Airbrake, 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 Airbrake. 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.
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Works with 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 10 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
The Pain of Context Switching
Right now, checking the health of a service is a clicking nightmare. You log into your monitoring dashboard, navigate to the correct project, select the environment (prod, staging), and then you're stuck reading graphs. If you need to know what changed right before the spike, you have to open a separate deployment history tab and manually cross-reference version numbers with error timestamps.
With this MCP, your agent handles that context switching for you. You tell it: 'Show me the top 5 errors in staging related to project X.' It executes the calls, pulls together the data points—the group details, the environment list, and the failure count—and gives you one clean answer without you opening a single dashboard.
Get Detailed Error Group Analysis with Airbrake
Previously, seeing an error group name was useless. You'd see 'TimeoutError (10k occurrences)', but you wouldn't know *why* or which specific users were impacted. You’d have to manually click into the group details to find the stack trace and affected user IDs.
Now, when you ask your agent about a specific error group using get_error_group, it provides the full context right away: the complete stack trace, the number of times it's happened, and which users were impacted. You get actionable data immediately.
What your AI can actually do with this
You don't want to switch context just to check if the latest release broke something. This connector lets your agent manage your entire error monitoring workflow without you leaving your code editor. Need to know which environment is failing? You can list all configured environments and pull up detailed project configs instantly.
Want to see what happened after the last deployment? The MCP tracks those installs, allowing you to correlate new errors with specific versions. If a developer finds an issue while testing, they can report it directly through your agent. Because Vinkius manages this entire catalog of tools, you get one connection point for all your system health needs—from project oversight to deep error inspection.
019dd0b6-33bd-7342-ae1e-fa1a237f9560 Here's how it actually works
The bottom line is you tell your agent what data you need—error counts, deployment IDs, etc.—and it fetches the result directly from Airbrake without you touching a dashboard.
Subscribe to this MCP and enter your API Key from Airbrake account settings.
Connect the MCP to any compatible client (like Cursor or VS Code).
Ask your AI agent to perform a task, such as listing error groups for a specific project.
Who is this actually for?
This MCP is for the DevOps Engineer who doesn't have time to click through multiple monitoring dashboards. It’s for the SRE tired of context switching when an alert goes off at 2 am. It helps QA Analysts validate environments before release, and developers who need immediate error data while debugging.
Tracks deployment status via list_deploys and correlates new errors using track_deploy to pinpoint which release caused the issue.
Periodically checks system health with check_airbrake_status and uses list_error_groups to find systemic failure patterns across environments.
Validates error groups for specific projects using get_project, ensuring that newly configured test environments are functioning before a major release.
Quickly reports custom bugs or notices of failure by calling report_notice so the team can track it immediately.
What Changes When You Connect
Pinpoint the source of failures: Instead of guessing, you can use list_error_groups to see which specific error type is most frequent for a project. This cuts investigation time from hours to minutes.
Maintain an audit trail: Use track_deploy whenever a release happens. This MCP links every reported failure back to the exact version and environment it occurred in.
Skip dashboard clicks: Need to know if your API gateway is healthy? You can run check_airbrake_status directly through your agent, confirming connectivity before you even start coding.
Deep dive into failures: Don't just see an error count. With get_error_group, you pull the full stack trace and affected users right into your chat window for immediate analysis.
Manage complexity: When you list_projects, you get a centralized view of every service monitored, helping you know exactly which application to focus on next.
See it in action
Post-release failure investigation
A DevOps engineer notices an increase in errors. They ask the agent to list_deploys for the service and then use get_error_group on the top error type, immediately correlating the spike with version 3.2.1 which was deployed last night.
Validating a new environment
A QA analyst needs to check staging before launch. They ask the agent to list_environments and then use list_error_groups against the 'staging' profile, ensuring all expected error groups are below threshold.
Capturing ad-hoc bugs
A developer finds a bug during local testing that isn't tracked yet. They call report_notice directly through their agent, providing immediate context and allowing the team to monitor it as if it were an automated failure.
Project health overview
A manager needs a quick status update on three microservices. The agent runs list_projects, giving them the IDs of all services, followed by running list_error_groups for each one to assess overall risk.
The honest tradeoffs
Checking system health via manual dashboard login
The team waits until a critical alert fires and then someone manually logs into the Airbrake console, gets stressed, and copies three different metrics across three separate tabs.
Instead, have your agent run check_airbrake_status to confirm connectivity first. If that passes, ask it to list_error_groups for the project name you suspect is having issues.
Assuming a bug report is sufficient
A developer finds an error and just sends a screenshot or text snippet to Slack, which requires multiple people to manually triage it into the monitoring system.
Use the report_notice tool. This ensures the notice gets logged with proper metadata immediately for tracking.
Debugging without version context
The team sees a spike in errors but doesn't know if it started after their last deployment or weeks ago, forcing them to waste time ruling out old code.
First, use list_deploys to get the recent history. Then, ask your agent to track_deploy with the version you suspect is the culprit to isolate the failure window.
When It Fits, When It Doesn't
Use this MCP if your primary goal is observability: correlating runtime errors with specific application versions and environments. You need to know why something broke, not just that it broke. Use list_error_groups when you want a high-level count of failure types, but use get_error_group when you need the actual stack trace for debugging. Don't rely on this MCP if you only need simple uptime metrics; those are better handled by dedicated monitoring platforms. However, if your process involves multiple stages—like development, staging, and production—you absolutely must use list_environments alongside track_deploy to maintain an auditable record.
Questions you might have
How do I list all projects using list_projects? +
The tool gives you a clean roster of every monitored service in your account. This is useful for getting an initial scope of work and figuring out which project IDs you need to investigate next.
What's the difference between list_error_groups and get_error_group? +
list_error_groups gives you a summary count, showing all unique error types for a project. get_error_group lets you dive into one specific type to see detailed information like stack traces.
Can I track deployments manually with track_deploy? +
Yes, you can use track_deploy whenever an install happens, specifying the version and environment. This ensures your error tracking is always correlated against a known release date.
Do I need to call check_airbrake_status first? +
It's good practice. Calling check_airbrake_status confirms that the MCP connection isn't stale or broken before you start asking for complex data like list_notices.
When should I use the `report_notice` tool if I find a bug manually? +
You use report_notice when you need to track an error that Airbrake hasn't automatically captured. This lets you report custom errors right away, giving it full context for monitoring purposes.
What specific data points does the `get_error_group` tool provide? +
The get_error_group tool gives deep details on an error group. You get full stack traces, frequency counts, and a list of users who were affected by that particular type of error.
How do I check all the operational stages or environments using `list_environments`? +
Running list_environments shows you every configured stage for your project, like production, staging, and development. This is key for understanding where an error might be limited to.
Does the `list_deploys` tool help me correlate errors with specific versions? +
Yes, list_deploys provides a history of all recorded releases. You can see which version was deployed and when, letting you trace error spikes back to a faulty build.
Can my AI show me the most recent error groups for a project? +
Yes. Use the list_error_groups tool with the project ID. The agent returns all error groups with occurrence counts, severity levels, and the last time each error was seen.
How do I track a Installment through the AI? +
Use the track_Install tool with the project ID, version string, and environment name. The agent records the Installment in Airbrake so you can correlate it with error rate changes.
Can I report a custom error to Airbrake via my AI agent? +
Yes. The report_notice tool sends a custom error with a type and message to any project. This is useful for tracking non-exception events or test failures.
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