How to build Featurebase dashboards in Metabase

Featurebase is a product feedback and changelog platform for collecting feature requests, running roadmaps, and announcing releases. Metabase is where you turn those customer signals into shared, trustworthy dashboards. This guide covers two complementary paths: a lightweight MCP + CLI route that pulls live data with the Featurebase MCP server and loads a CSV into Metabase with the Metabase CLI, and a durable pipeline route that syncs Featurebase data into a database so you can build dashboards anyone can read.

How do you connect Featurebase to Metabase?

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

What can you analyze from Featurebase data in Metabase?

• Feature request pipeline by status — built from posts (feature requests) and the related upvotes, comments, changelog entries data your sync exposes.
• Upvote velocity on open posts — built from posts (feature requests) and the related upvotes, comments, changelog entries data your sync exposes.
• Requests by board and segment — built from posts (feature requests) and the related upvotes, comments, changelog entries data your sync exposes.
• Changelog cadence and reach — built from posts (feature requests) and the related upvotes, comments, changelog entries data your sync exposes.
• Time from request to shipped — built from posts (feature requests) and the related upvotes, comments, changelog entries data your sync exposes.

Which Featurebase dashboards should you build in Metabase?

How do you use the Featurebase MCP server with the Metabase CLI?

Pair the Featurebase 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 posts (feature requests) by vote activity this quarter, with status, product area, and segment fields.
• Export the result as CSV, keeping stable IDs, statuses, segments, 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.
• Status-change timestamps are required for time-to-close-the-loop and pipeline aging trends.
• mb upload csv needs an uploads database configured under Admin → Settings → Uploads.

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

{
  "mcpServers": {
    "featurebase": {
      "url": "PASTE_YOUR_READER_CONNECTOR_URL"
    }
  }
}

Available on Professional plans and above. Featurebase generates workspace-specific connector URLs under Settings → MCP and splits them into Reader and Writer servers — use the read-only Reader URL for analysis so the assistant can't change posts or statuses. Setup verified July 2026.

# 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 posts-feature-requests export — creates a table AND a model
mb upload csv --file featurebase-posts-feature-requests.csv --collection root

# Refresh that same table later from a new export
mb upload replace <table-id> --file featurebase-posts-feature-requests.csv

Can you generate a Featurebase dashboard with AI?

Yes. Use the prompt below with any assistant that can run the Featurebase MCP server and the Metabase CLI. It works end to end: if Featurebase 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 Featurebase product feedback analytics.
Work end to end: get the data into Metabase if it isn't there yet, then build.

Goal: Help product teams understand request pipeline health, voice-of-customer themes, roadmap alignment, and close-the-loop speed from Featurebase data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for featurebase tables and
  models). If durable Featurebase 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 Featurebase MCP server:
  posts (feature requests), plus upvotes, comments, changelog entries.
  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, statuses,
segments, and timestamps, and whether history exists before creating duration
or trend cards.

Important:
- Build on whatever data is present; don't claim Metabase connects natively to
  Featurebase — it reads a database or CLI-uploaded tables.
- Never load raw event or session streams into Metabase; use rollups, entity
  tables, and response- or request-grain data.
- Compute NPS as % promoters (9-10) minus % detractors (0-6) on the 0-10 scale,
  and always show response volume next to the score.
- Only compute durations (time to close, time to complete) when the required
  timestamps exist.
- Exclude internal, test, and anonymous-preview activity from headline cards,
  and segment by plan or customer segment 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: Featurebase Product Feedback Overview

Sections:
1. Executive summary: Open requests; New requests last 30 days; Shipped last
   quarter; Median votes per open request; Median time to close.
2. Pipeline: Requests by status; aging buckets; status-change flow by week.
3. Voice of customer: Volume by product area and segment; top requests by
   vote velocity.
4. Roadmap alignment: Share linked to roadmap items; votes on planned vs.
   unplanned work.
5. Close the loop: Time from request to shipped; shipped-with-notification
   share.

Filters: Date range, Product area, Segment, Status, Survey or Feature.

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 Featurebase data into a database or warehouse?

For dashboards that need history and reliability, land Featurebase data in a database first, then connect Metabase to that database.

Connector options

• Managed ETL or native export — use a connector or the vendor's own warehouse sync when one covers the objects you need.
• Custom pipeline — use the Featurebase API for control over objects, fields, and refresh cadence.
• MCP + CSV — use this for quick exploration and one-off slices.

No managed connector exists — script the Featurebase API for posts, upvotes, comments, and changelog entries on a schedule, and land weekly snapshots so pipeline and velocity trends have history.

Notes

• Decide the grain first — request, response, or daily rollup — it drives every trend card and the warehouse bill.
• Land raw entity tables first, then build clean Metabase models on top.
• Normalize product area, status, source, customer segment, vote-count, and status-changed-at fields.

How should you model Featurebase data in Metabase?

Core tables

Modeling advice

• Build a clean feedback_items model with common columns across tools, so multi-source dashboards don't fork definitions.
• Separate entity tables (requests, surveys, features, accounts) from time-series rollups and event-grain tables.
• Exclude internal users, test workspaces, and preview traffic from headline metrics; keep segment as an explicit column.
• Use stable IDs for account, request, and survey joins; display names change.

Which Featurebase metrics should you track in Metabase?

What SQL powers Featurebase dashboards in Metabase?

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

SELECT
  status,
  COUNT(*) AS open_requests,
  percentile_cont(0.5) WITHIN GROUP (
    ORDER BY CURRENT_DATE - created_at::date
  ) AS median_age_days
FROM feedback_items
WHERE status NOT IN ('shipped', 'closed')
GROUP BY status
ORDER BY open_requests DESC;

SELECT
  f.title,
  f.product_area,
  COUNT(*) AS votes_last_30_days
FROM votes v
JOIN feedback_items f ON f.id = v.feedback_item_id
WHERE v.created_at >= CURRENT_DATE - INTERVAL '30 days'
  AND f.status NOT IN ('shipped', 'closed')
GROUP BY f.title, f.product_area
ORDER BY votes_last_30_days DESC
LIMIT 20;

-- The loop closes when requesters are told, so measure to announced_at.
-- No announcement tracking? Swap in shipped_at and call it time to ship.
SELECT
  date_trunc('quarter', announced_at) AS quarter,
  COUNT(*) AS closed_loop_requests,
  percentile_cont(0.5) WITHIN GROUP (
    ORDER BY EXTRACT(EPOCH FROM (announced_at - created_at)) / 86400
  ) AS median_days_to_close_loop
FROM feedback_items
WHERE shipped_at IS NOT NULL
  AND announced_at IS NOT NULL
GROUP BY 1
ORDER BY 1;

What are common mistakes when analyzing Featurebase in Metabase?

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FAQ