How to build Miro dashboards in Metabase
Miro is a visual collaboration workspace for boards, frames, sticky notes, diagrams, cards, comments, and workshops. Metabase is where you turn that operational activity into shared, trustworthy dashboards. This guide covers two complementary paths: a lightweight MCP + CLI routethat pulls live data with the Miro MCP Server and loads a CSV into Metabase with the Metabase CLI, and a durable pipeline route that syncs Miro into a database so you can build dashboards anyone can read.
How do you connect Miro to Metabase?
Most teams combine both routes: use MCP and CLI uploads for a fast first pass, then move recurring reporting to a warehouse-backed model.
Live data in, quick analysis out
Pair the Miro MCP Server with the Metabase CLI. Use MCP for live lookups, write the result to CSV, then load it into Metabase as a ready-to-query table and model.
- Quick lookups such as "show me board and workspace activity"
- Loading a Miro CSV export into Metabase in seconds
- Spot-checks and one-off analyses without a warehouse
- Great for exploration, not governed reporting
- Use read-only/scoped credentials wherever the MCP server supports them
- CSV uploads are snapshots — refresh or move to the pipeline for history
Durable dashboards with history
Sync Miro into a database or warehouse with a managed connector, custom pipeline, or API, then point Metabase at it.
- Miro dashboards leaders depend on
- Joining Miro data with product, support, sales, or engineering data
- Long-run trends for board and workspace activity and stale boards by owner
- You own the refresh schedule and clean data model
- Change and activity history need explicit snapshots, events, or changelog syncs
- Metric definitions must be consistent across tools and teams
What can you analyze from Miro data in Metabase?
- Board and workspace activity — built from boards and the related frames, items, cards data your sync exposes.
- Stale boards by owner — built from boards and the related frames, items, cards data your sync exposes.
- Workshop participation — built from boards and the related frames, items, cards data your sync exposes.
- Comment and follow-up activity — built from boards and the related frames, items, cards data your sync exposes.
- Board ownership and lifecycle — built from boards and the related frames, items, cards data your sync exposes.
Which Miro dashboards should you build in Metabase?
Miro overview
The shared operating view to build first.
- Board and workspace activity
- Stale boards by owner
- Workshop participation
Coverage, freshness, and activity
The deeper views that expose risk and cleanup work.
- Comment and follow-up activity
- Board ownership and lifecycle
How do you use the Miro MCP Server with the Metabase CLI?
Pair the Miro MCP Server with the Metabase CLI for fast, hands-on analysis. MCP is useful for scoped lookups and 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 recently updated, stale, or ownerless boards.
- Export the result as CSV, keeping stable IDs, owners, containers, status or type, and timestamps.
- Run
mb upload csvto 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 replaceor move to the pipeline for real history. - Change events or snapshots are required for reliable freshness and activity trends.
mb upload csvneeds an uploads database configured under Admin → Settings → Uploads.
How do you set up Miro MCP and the Metabase CLI?
Miro MCP Serverofficial
- Transport
- Hosted remote MCP via Streamable HTTP
- Auth
- OAuth 2.1 scoped to a Miro team
- Best for
- Live scoped lookup and export
Metabase CLIofficial
- Install
npm install -g @metabase/cli- Auth
mb auth login- Load data
mb upload csv --file data.csv- Requires
- An uploads database (Admin → Settings → Uploads)
{
"mcpServers": {
"miro": {
"url": "https://mcp.miro.com/"
}
}
}# 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 boards export — creates a table AND a model
mb upload csv --file miro-boards.csv --collection root
# Refresh that same table later from a new export
mb upload replace <table-id> --file miro-boards.csvCan you generate a Miro dashboard with AI?
Yes. Use the prompt below with any assistant that can run the Miro MCP Server and the Metabase CLI. It works end to end: if Miro tables already exist in Metabase it analyzes those; otherwise it pulls scoped 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 Miro content & knowledge analytics.
Work end to end: get the data into Metabase if it isn't there yet, then build.
Goal: Help team and program leaders understand content freshness, ownership, knowledge coverage, workspace activity, and stale-content risk from Miro data.
Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for miro tables and
models). If durable Miro 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 export with the Miro MCP Server: boards,
frames, items, cards. 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, dates, owners,
containers, status values, and whether change/activity history exists before creating
duration or trend cards.
Important:
- Build on whatever data is present; don't claim Metabase connects natively to
Miro — it reads a database or CLI-uploaded tables.
- Only compute durations when the required start/end or event timestamps exist.
- Exclude archived, deleted, canceled, or test objects from active-population cards.
- Avoid individual performance leaderboards; use owner views for balancing and cleanup.
- 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: Miro Content & Knowledge Overview
Sections:
1. Executive summary: Total active content; Freshness rate; Owned-content rate;
Content updated last 30 days; Stale high-priority content.
2. Freshness: Content by age bucket; stale content by workspace/container and owner.
3. Coverage: Missing owners, descriptions, classifications, or required topic coverage.
4. Activity: Created vs updated by week; active contributors and containers.
5. Governance: External sharing, orphaned content, and permission risk when available.
Filters: Date range, Project/board/database, Status, Owner, Team, Work type.
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 Miro data into a database or warehouse?
For dashboards that need history and reliability, land Miro data 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 Miro REST API for control over fields, history, and refresh cadence.
- MCP + CSV — use this for quick exploration and one-off slices.
Sync boards, items, frames, cards, tags, comments, users, and activity with the REST API and webhooks; snapshot board metadata for stale-board trends.
Notes
- Land raw tables first, then build clean Metabase models on top.
- Keep change events or daily snapshots if you need trends and duration metrics.
- Normalize owner, container, content type, lifecycle, created, updated, and reviewed-date fields.
How should you model Miro data in Metabase?
Core tables
| Table | Grain | Key columns |
|---|---|---|
miro_boards | one row per board | board_id, team_id, name, owner_id, created_at, modified_at, view_link |
miro_items | one row per board item | item_id, board_id, item_type, parent_id, creator_id, created_at, modified_at |
miro_comments | one row per comment | comment_id, board_id, item_id, user_id, created_at, resolved_at |
Modeling advice
- Build a clean
content_itemsmodel with common columns across tools. - Separate current-state fields from change events, activity, or snapshot history.
- Exclude archived, deleted, test, and duplicate objects from active-population metrics.
- Use stable IDs for owner, project, and item joins; display names can change.
Which Miro metrics should you track in Metabase?
| Metric | Definition | Notes |
|---|---|---|
| Documentation freshness | Active content updated inside an agreed freshness window. | Set different windows by content class. |
| Knowledge-base coverage | Required topics or objects with current, owned documentation. | Define the required inventory first. |
| Workspace activity rate | Active containers ÷ eligible containers in a period. | Separate healthy quiet from abandoned. |
What SQL powers Miro dashboards in Metabase?
These assume a cleaned analytical model in a warehouse (PostgreSQL dialect). Adjust table and column names to match your connector.
The share of active content updated in the last 90 days.
SELECT
container_name,
COUNT(*) AS content_items,
COUNT(*) FILTER (
WHERE updated_at >= CURRENT_DATE - INTERVAL '90 days'
) AS fresh_items,
ROUND(
100.0 * COUNT(*) FILTER (
WHERE updated_at >= CURRENT_DATE - INTERVAL '90 days'
) / NULLIF(COUNT(*), 0),
2
) AS freshness_rate
FROM content_items
WHERE archived_at IS NULL
GROUP BY container_name
ORDER BY freshness_rate ASC;Active content with an accountable owner.
SELECT
container_name,
COUNT(*) AS content_items,
COUNT(*) FILTER (WHERE owner_id IS NOT NULL) AS owned_items,
ROUND(
100.0 * COUNT(*) FILTER (WHERE owner_id IS NOT NULL)
/ NULLIF(COUNT(*), 0),
2
) AS ownership_coverage
FROM content_items
WHERE archived_at IS NULL
GROUP BY container_name
ORDER BY ownership_coverage ASC;Events and distinct contributors by container.
SELECT
date_trunc('week', occurred_at) AS week,
container_name,
COUNT(*) AS activity_events,
COUNT(DISTINCT actor_id) AS active_contributors
FROM collaboration_events
GROUP BY 1, 2
ORDER BY 1, 2;