How to build Raygun dashboards in Metabase

Raygun is an error, crash, and real-user monitoring platform that ties failures to releases and affected customers. Metabase is where you turn those operational signals into shared, trustworthy dashboards. This guide covers two complementary paths: a lightweight MCP + CLI route that pulls live data with the Raygun MCP Server and loads a CSV into Metabase with the Metabase CLI, and a durable pipeline route that syncs Raygun rollups into a database so you can build dashboards anyone can read.

How do you connect Raygun to Metabase?

Most teams combine both routes: use MCP and CLI uploads for a fast first pass, then move recurring reliability reporting to a warehouse-backed model.

What can you analyze from Raygun data in Metabase?

• Error inflow and resolution — built from error groups and the related error instances, deployments, RUM sessions data your sync exposes.
• Deployment and release quality — built from error groups and the related error instances, deployments, RUM sessions data your sync exposes.
• Top errors by affected users — built from error groups and the related error instances, deployments, RUM sessions data your sync exposes.
• Real-user performance (RUM) — built from error groups and the related error instances, deployments, RUM sessions data your sync exposes.
• Application health overview — built from error groups and the related error instances, deployments, RUM sessions data your sync exposes.

Which Raygun dashboards should you build in Metabase?

How do you use the Raygun MCP Server with the Metabase CLI?

Pair the Raygun MCP Server with the Metabase CLI for fast, hands-on analysis. MCP is useful for scoped lookups and summarized exports; the Metabase CLI's upload command loads CSV data into Metabase and creates a ready-to-query table and model.

Example workflow

• Ask the MCP server for the top error groups by affected users this week.
• Export the result as CSV, keeping stable IDs, services, environments, severities, and timestamps.
• Run mb upload csv to load it into Metabase as a table and model, then build questions and dashboards on top.

Be honest about the limits

• MCP lookups are excellent for exploration, not scheduled reporting.
• A CSV upload is a snapshot; refresh it with mb upload replace or move to the pipeline for real history.
• Release tags and first-seen/last-seen timestamps are required for regression and error-rate trends.
• mb upload csv needs an uploads database configured under Admin → Settings → Uploads.

How do you set up Raygun MCP and the Metabase CLI?

{
  "mcpServers": {
    "raygun": {
      "url": "https://api.raygun.com/v3/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_PAT_TOKEN"
      }
    }
  }
}

The PAT grants broad account access — keep it out of version control and scope analysis to read-style tools.

# Install the Metabase CLI
npm install -g @metabase/cli

# Log in (opens your browser; requires Metabase v62+)
mb auth login --url https://your-metabase.example.com

# Load a error-groups export — creates a table AND a model
mb upload csv --file raygun-error-groups.csv --collection root

# Refresh that same table later from a new export
mb upload replace <table-id> --file raygun-error-groups.csv

Can you generate a Raygun dashboard with AI?

Yes. Use the prompt below with any assistant that can run the Raygun MCP Server and the Metabase CLI. It works end to end: if Raygun tables already exist in Metabase it analyzes those; otherwise it pulls scoped, summarized data over MCP, loads it with mb upload csv, then builds the dashboard and caveats any metric that needs missing history.

Create a polished Metabase dashboard for Raygun error monitoring analytics.
Work end to end: get the data into Metabase if it isn't there yet, then build.

Goal: Help engineering and operations leaders understand error inflow, release quality, user impact, and resolution speed from Raygun data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for raygun tables and
  models). If durable Raygun data is already present — synced from a warehouse
  or uploaded earlier — use it and skip to Step 2.
- If nothing is there, pull a scoped, summarized export with the Raygun MCP Server:
  error groups, plus error instances, deployments, RUM sessions.
  Prefer aggregated or rollup views over raw events. Write each result to a CSV,
  then load it with the Metabase CLI — run "mb upload csv --file <export>.csv" so
  each upload creates a table and a ready-to-query model. Use "mb upload replace
  <table-id> --file <export>.csv" to refresh an existing table instead of creating
  duplicates.

Step 2 — Inspect before querying:
Do not assume exact table or column names. Inspect available fields, services,
environments, timestamps, and whether rollups or history exist before creating
duration or trend cards.

Important:
- Build on whatever data is present; don't claim Metabase connects natively to
  Raygun — it reads a database or CLI-uploaded tables.
- Never try to load the raw event firehose into Metabase; use rollups, entity
  tables, and incident- or group-grain data.
- Only compute durations (MTTA, MTTR, time-to-resolve) when the required
  timestamps exist.
- Exclude test, staging, or muted objects from headline reliability cards, and
  segment by environment where the field exists.
- A single CSV is a point-in-time snapshot: only build trend cards if there is a
  usable date column or multiple periods have been uploaded.

Dashboard title: Raygun Error Monitoring Overview

Sections:
1. Executive summary: Open error groups; New groups last 7 days; Regressions;
   Affected users last 7 days; Median time to resolve.
2. Inflow: New vs resolved error groups by week; error events by service.
3. Release quality: Error rate by release; new groups per release; crash-free rate
   if session data exists.
4. Impact: Top groups by affected users; unassigned groups; stale open groups.
5. Resolution: Time from first-seen to resolved; reopened/regressed groups.

Filters: Date range, Service, Environment, Severity, Team, Status.

Output: Build the dashboard if you have permission; otherwise provide the exact
questions, SQL, model definitions, and layout. Include caveats for any metric
that cannot be calculated from the available data.

How do you sync Raygun data into a database or warehouse?

For dashboards that need history and reliability, land Raygun rollups and metadata in a database first, then connect Metabase to that database.

Connector options

• Managed ETL — use a connector when one covers the objects you need.
• Custom pipeline — use the Raygun Public API for control over rollup grain, fields, and refresh cadence.
• MCP + CSV — use this for quick exploration and one-off slices.

No managed connector exists — script the Raygun public API for error groups, instances, deployments, and RUM summaries, or use webhooks for push-based events.

Notes

• Decide the rollup grain first (hourly or daily per service/environment) — it drives warehouse cost and every trend card.
• Land raw entity tables first, then build clean Metabase models on top.
• Normalize service, release, environment, severity, first-seen, and last-seen fields.

How should you model Raygun data in Metabase?

Core tables

Modeling advice

• Build a clean error_rollups model with common columns across tools, so multi-source dashboards don't fork definitions.
• Separate entity tables (services, monitors, policies) from time-series rollups and event-grain tables.
• Exclude test, staging, and muted objects from headline reliability metrics; keep environment as an explicit column.
• Use stable IDs for service, team, and incident joins; display names change.

Which Raygun metrics should you track in Metabase?

What SQL powers Raygun dashboards in Metabase?

These assume a cleaned analytical model in a warehouse (PostgreSQL dialect). Adjust table and column names to match your pipeline.

SELECT
  date_trunc('week', first_seen_at) AS week,
  COUNT(*) AS new_error_groups,
  COUNT(*) FILTER (WHERE resolved_at IS NOT NULL) AS since_resolved
FROM error_groups
WHERE environment = 'production'
GROUP BY 1
ORDER BY 1;

SELECT
  r.version AS release,
  SUM(e.event_count) AS error_events,
  COUNT(DISTINCT e.error_group_id) AS distinct_groups,
  ROUND(
    1.0 * SUM(e.event_count) / NULLIF(MAX(r.sessions), 0), 4
  ) AS errors_per_session
FROM error_rollups e
JOIN releases r ON r.id = e.release_id
WHERE e.environment = 'production'
GROUP BY r.version
ORDER BY MIN(r.released_at) DESC;

SELECT
  g.title,
  g.service,
  SUM(e.affected_users) AS affected_users,
  SUM(e.event_count) AS events
FROM error_rollups e
JOIN error_groups g ON g.id = e.error_group_id
WHERE e.window_start >= CURRENT_DATE - INTERVAL '30 days'
  AND g.resolved_at IS NULL
GROUP BY g.title, g.service
ORDER BY affected_users DESC
LIMIT 20;

What are common mistakes when analyzing Raygun in Metabase?

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FAQ