How to build Semrush dashboards in Metabase

Semrush is an online-visibility platform covering keyword research, position tracking, backlinks, and competitive intelligence. Metabase is where you turn that SEO data into shared, trustworthy dashboards. This guide covers two complementary paths: a lightweight MCP + CLI route that pulls live data with the Semrush MCP server and loads a CSV into Metabase with the Metabase CLI, and a durable pipeline route that syncs Semrush snapshots into a database so you can build dashboards anyone can read.

How do you connect Semrush to Metabase?

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

What can you analyze from Semrush data in Metabase?

• Organic keywords and estimated traffic trend — built from domain visibility and position-tracking rows and the related organic keyword counts, position tracking campaigns, backlink summaries data your sync exposes.
• Position-tracking visibility by campaign — built from domain visibility and position-tracking rows and the related organic keyword counts, position tracking campaigns, backlink summaries data your sync exposes.
• Keyword movers: gains and losses by week — built from domain visibility and position-tracking rows and the related organic keyword counts, position tracking campaigns, backlink summaries data your sync exposes.
• Backlink authority and referring-domain growth — built from domain visibility and position-tracking rows and the related organic keyword counts, position tracking campaigns, backlink summaries data your sync exposes.
• Share of voice against tracked competitors — built from domain visibility and position-tracking rows and the related organic keyword counts, position tracking campaigns, backlink summaries data your sync exposes.

Which Semrush dashboards should you build in Metabase?

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

Pair the Semrush 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 latest domain visibility and position-tracking rows for your tracked keywords, with search volume and position deltas.
• Export the result as CSV, keeping keywords, domains, positions, and snapshot dates as stable columns.
• 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.
• Position and authority trends need dated snapshots — vendor UIs keep history, but your warehouse only trends what you've synced, so schedule pulls before you need the chart.
• mb upload csv needs an uploads database configured under Admin → Settings → Uploads.

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

{
  "mcpServers": {
    "semrush": {
      "url": "https://mcp.semrush.com/v2/mcp",
      "headers": {
        "Authorization": "Apikey YOUR_API_KEY"
      }
    }
  }
}

API access is plan-gated: SEO data needs a Semrush One Starter/Pro plan or higher (or an API-units package; legacy SEO Classic Pro/Guru plans also qualify) with 50,000 API units included, and Trends traffic data needs a separate Trends API subscription. Budget units before pointing an agent at wide keyword pulls.

# 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 domain-visibility-and-position-tracking-rows export — creates a table AND a model
mb upload csv --file semrush-domain-visibility-and-position-tracking-rows.csv --collection root

# Refresh that same table later from a new export
mb upload replace <table-id> --file semrush-domain-visibility-and-position-tracking-rows.csv

Can you generate a Semrush dashboard with AI?

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

Goal: Help SEO and marketing leaders understand keyword visibility, position movement, backlink growth, and share of voice against competitors from Semrush data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for semrush tables and
  models). If durable Semrush 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 Semrush MCP server:
  domain visibility and position-tracking rows, plus organic keyword counts, position tracking campaigns, backlink summaries.
  Prefer dated snapshots and rollups over wide raw pulls — API units cost money.
  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 keywords, domains,
dates, and whether multiple snapshot dates exist before creating trend cards.

Important:
- Build on whatever data is present; don't claim Metabase connects natively to
  Semrush — it reads a database or CLI-uploaded tables.
- Trend cards need multiple dated snapshots; a single export only supports
  point-in-time cards — label them as such.
- Keep vendor metrics separate from Search Console data: estimated traffic and
  search volume are modeled numbers, clicks and impressions are measured ones.
- State the position convention (best rank per keyword vs. all appearances)
  once and reuse it on every card.
- Segment branded from non-branded keywords before reporting averages.

Dashboard title: Semrush SEO Overview

Sections:
1. Executive summary: Estimated organic traffic; Keywords in top 10; Domain
   authority; Referring domains; Share of voice.
2. Visibility: Top 3/10/20 keyword counts over time; average position trend.
3. Movement: Biggest gains and losses; newly ranking; striking-distance table.
4. Authority: Authority-score trend; new vs. lost referring domains; velocity.
5. Competitors: Share of voice and visibility comparison across rivals.

Filters: Date range, Domain/Project, Keyword group, Search engine, Location,
Competitor.

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 Semrush data into a database or warehouse?

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

No managed Airbyte or Fivetran connector exists (the Airbyte source is an open request, not a shipped connector) — schedule scripts against the Semrush API and land daily domain and position-tracking rollups, watching per-call API-unit costs.

Notes

• Decide the snapshot grain first (daily per keyword for rankings, weekly for backlinks is the workhorse) — it drives API-unit spend and every trend card.
• Land raw snapshot tables first, then build clean Metabase models on top.
• Normalize keyword, target domain, snapshot date, position, search volume, and URL fields.

How should you model Semrush data in Metabase?

Core tables

Modeling advice

• Build a clean seo_keyword_positions model with common columns across tools, so multi-tool SEO dashboards don't fork definitions.
• Every snapshot table needs an explicit snapshot or crawl date column — trends live or die on it.
• Keep modeled vendor estimates (volume, traffic, difficulty) in separate columns from measured Search Console data.
• Store keyword and domain join keys in a consistent normalized form (lowercase, no protocol, no trailing slash).

Which Semrush metrics should you track in Metabase?

What SQL powers Semrush dashboards in Metabase?

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

SELECT
  stat_date,
  COUNT(*) FILTER (WHERE position <= 3) AS top_3,
  COUNT(*) FILTER (WHERE position <= 10) AS top_10,
  COUNT(*) FILTER (WHERE position BETWEEN 11 AND 20) AS striking_distance
FROM seo_keyword_positions
WHERE target_domain = 'yoursite.com'
GROUP BY 1
ORDER BY 1;

SELECT
  stat_date,
  referring_domains,
  referring_domains - LAG(referring_domains) OVER (
    PARTITION BY domain ORDER BY stat_date
  ) AS net_ref_domain_change,
  backlinks_total,
  authority_score
FROM semrush_backlinks_summary
WHERE domain = 'yoursite.com'
ORDER BY stat_date;

WITH weighted AS (
  SELECT
    stat_date,
    target_domain,
    -- weight each ranking by its keyword's volume and a CTR-by-position curve
    SUM(search_volume * CASE
      WHEN position = 1 THEN 0.28
      WHEN position <= 3 THEN 0.15
      WHEN position <= 10 THEN 0.05
      ELSE 0.01 END) AS weighted_clicks
  FROM seo_keyword_positions
  GROUP BY 1, 2
)
SELECT
  stat_date,
  target_domain,
  ROUND(100.0 * weighted_clicks
    / NULLIF(SUM(weighted_clicks) OVER (PARTITION BY stat_date), 0), 1)
    AS share_of_voice_pct
FROM weighted
ORDER BY 1, 3 DESC;

What are common mistakes when analyzing Semrush in Metabase?

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