Query errors and get full context.
Ask your agent to pull the details on any Rollbar item: stack trace, request parameters, telemetry timeline, occurrence information and count, and affected environment. All right from your IDE or terminal.
Check what shipped and what broke.
Pull recent deploys, check version status across environments, and correlate errors with specific releases. Your agent sees the same deploy timeline your team uses to catch regressions.
Find the noisiest problems.
Get the top items from the last 24 hours for any environment, filtered by status, level, or search query. Your agent can triage the error feed without you opening the dashboard.
Get ahead of user experience issues.
Pull session replay data into the agent's context to understand why an error happened, not just that it did. Your assistant traces the user's navigation path, clicks, and DOM state to identify the UX problem that led to the failure. A confusing flow, missing validation, or a race condition from rapid interactions stops being a mystery and becomes a fix.
Set up in under two minutes.
The MCP server runs locally through your AI tool. No separate process. No HTTP server. One config block and you're connected.
Grab an Account Access read/write token from your settings.
If you don't have a Rollbar account, sign up for free. (5,000 events/month, unlimited projects and users)
Using Claude Code as an example, configure your .mcp.json as follows:
{
"mcpServers": {
"rollbar": {
"type": "stdio",
"command": "npx",
"args": ["-y", "@rollbar/mcp-server@latest"],
"env": {
"ROLLBAR_ACCESS_TOKEN": "<project read/write access token>"
}
}
}
}
Check the detailed instructions for Codex CLI, Cursor, GitHub Copilot CLI, Junie, and VS Code.
Restart your editor, open a conversation, and ask your agent about your production errors. It now has direct access to all your Rollbar project(s) data.
For multi-project setups, config file options, and editor-specific paths, see the full setup docs.
AI coding agents are good at reasoning about code. They're bad at reasoning about production. They don't know which errors are active, which deploy introduced a regression, or what the user was doing when things broke.
Rollbar's MCP server closes that gap. It connects the context your agent needs to the data Rollbar already collects.
Rollbar groups errors, tracks deploys, and surfaces the signals that matter. That data already exists in your project.
Your agent can now pull that data directly: stack traces with frame context, telemetry timelines, deploy correlation, occurrence frequency. It doesn't need you to copy-paste anything.
With real production context, your agent's suggested fixes are grounded in what's actually happening. Not pattern-matched guesses from code alone, but analysis informed by the specific error, the specific user path, and the specific deploy.
Session Replay shows what happened. AI Root Cause Analysis tells you why. The MCP server lets your AI agent help you write the fix.
Built for engineers who read the setup before they trust it.
This is Rollbar's own official server, not a third-party wrapper. It is open source under the MIT license and maintained on GitHub.
Runs on your machine.
The server is a local stdio process that your AI tool launches directly.
Installs in one line.
Reference a single command from your tool's own MCP config file.
Uses a token you already control.
Authentication is a Rollbar access token that you scope yourself.
Read-only by default.
Write actions only work when you provide a write-scoped token.
Keeps credentials local.
Multi-project setups use a local .rollbar-mcp.json config file.
After a deploy goes sideways:
Check Rollbar for any new critical errors in production since the last deploy. For the highest-occurrence one, pull the full item details and suggest a fix.
Your agent calls get-deployments, identifies the latest release, calls list-items filtered to critical errors in that environment, then calls get-item-details on the top hit. It returns a diagnosis with the stack trace context and a code suggestion tied to the actual failure.
Triaging the morning error feed:
Show me the top 10 unresolved errors in production. For any that have more than 100 occurrences, pull the item details and group them by likely root cause.
Your agent calls get-top-items, filters for high-occurrence items, and pulls details for each. You get a prioritized summary without opening the dashboard.
Investigating a user-reported bug:
Get the details for Rollbar item #7842 and fetch the session replay. Walk me through what the user did before the error and suggest where the bug is.
Your agent calls get-item-details for the stack trace and telemetry, then get-replay for the session data. It correlates the user's navigation path with the error to identify the upstream cause.
Cursor
MCP settings
Claude Code
.mcp.json
VS Code
.vscode/mcp.json
Codex CLI
~/.codex/config.toml
Copilot
~/.copilot/mcp-config.json
＋ Any tool that supports the Model Context Protocol stdio server type.
Open source. MIT licensed. View on GitHub.
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