How to build Canny dashboards in Metabase

Canny is a customer feedback platform where users post and vote on feature requests across public or private boards. 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 Canny MCP server and loads a CSV into Metabase with the Metabase CLI, and a durable pipeline route that syncs Canny data into a database so you can build dashboards anyone can read.

How do you connect Canny 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 Canny data in Metabase?

• Feature request pipeline by status — built from posts (feature requests) and the related votes, comments, boards data your sync exposes.
• Vote velocity on open posts — built from posts (feature requests) and the related votes, comments, boards data your sync exposes.
• Requests by customer segment — built from posts (feature requests) and the related votes, comments, boards data your sync exposes.
• Time from post to shipped — built from posts (feature requests) and the related votes, comments, boards data your sync exposes.
• Board health and response rate — built from posts (feature requests) and the related votes, comments, boards data your sync exposes.

Which Canny dashboards should you build in Metabase?

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

Pair the Canny 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 Canny MCP and the Metabase CLI?

{
  "mcpServers": {
    "canny": {
      "url": "https://api.canny.io/api/mcp/v1"
    }
  }
}

The official server is double-gated: your workspace needs the Ideas beta and a Pro plan or above. On other plans, the community @opensourceops/canny-mcp server (local, read-only by default, using a Canny API key) covers the same analytics reads. One API quirk to know either way: every Canny endpoint is a POST with the apiKey in the request body. Endpoint 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 canny-posts-feature-requests.csv --collection root

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

Can you generate a Canny dashboard with AI?

Yes. Use the prompt below with any assistant that can run the Canny MCP server and the Metabase CLI. It works end to end: if Canny 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 Canny 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 Canny data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for canny tables and
  models). If durable Canny 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 Canny MCP server:
  posts (feature requests), plus votes, comments, boards.
  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
  Canny — 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: Canny 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 Canny data into a database or warehouse?

For dashboards that need history and reliability, land Canny 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 Canny API for control over objects, fields, and refresh cadence.
• MCP + CSV — use this for quick exploration and one-off slices.

Sync posts, votes, comments, and boards with the community Airbyte source or Fivetran's Lite connector, or script the Canny API — pagination is cursor-based and every call is a POST, so most teams wrap it in a small export script.

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 Canny 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 Canny metrics should you track in Metabase?

What SQL powers Canny 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 Canny in Metabase?

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FAQ