How to build Microsoft Teams dashboards in Metabase

Microsoft Teams is a Microsoft 365 collaboration hub for teams, channels, chats, meetings, messages, and files. 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 Work IQ Teams MCP Server and loads a CSV into Metabase with the Metabase CLI, and a durable pipeline route that syncs Microsoft Teams into a database so you can build dashboards anyone can read.

How do you connect Microsoft Teams 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.

What can you analyze from Microsoft Teams data in Metabase?

• Channel and team activity — built from messages and the related teams, channels, chats data your sync exposes.
• Collaboration response time — built from messages and the related teams, channels, chats data your sync exposes.
• Meeting and message volume — built from messages and the related teams, channels, chats data your sync exposes.
• Inactive teams and channels — built from messages and the related teams, channels, chats data your sync exposes.
• Cross-team participation — built from messages and the related teams, channels, chats data your sync exposes.

Which Microsoft Teams dashboards should you build in Metabase?

How do you use the Work IQ Teams MCP Server with the Metabase CLI?

Pair the Work IQ Teams 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 recent activity, unanswered threads, or inactive messages.
• Export the result as CSV, keeping stable IDs, owners, containers, status or type, 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.
• Thread and event timestamps are required for response-time and activity trends.
• mb upload csv needs an uploads database configured under Admin → Settings → Uploads.

How do you set up Microsoft Teams MCP and the Metabase CLI?

{
  "mcpServers": {
    "teams": {
      "url": "https://agent365.svc.cloud.microsoft/agents/tenants/{tenantId}/servers/mcp_TeamsServer"
    }
  }
}

# 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 messages export — creates a table AND a model
mb upload csv --file microsoft-teams-messages.csv --collection root

# Refresh that same table later from a new export
mb upload replace <table-id> --file microsoft-teams-messages.csv

Can you generate a Microsoft Teams dashboard with AI?

Yes. Use the prompt below with any assistant that can run the Work IQ Teams MCP Server and the Metabase CLI. It works end to end: if Microsoft Teams 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 Microsoft Teams collaboration activity 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 workspace activity, response time, participation, inactive areas, and collaboration load from Microsoft Teams data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for microsoft-teams tables and
  models). If durable Microsoft Teams 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 Work IQ Teams MCP Server: messages,
  teams, channels, chats. 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
  Microsoft Teams — 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: Microsoft Teams Collaboration Activity Overview

Sections:
1. Executive summary: Active spaces; Activity last 30 days; Median response time;
   Inactive spaces; Active contributors.
2. Activity: Events by week, workspace, channel, or project.
3. Response: First-response distribution and unresolved threads when available.
4. Participation: Contributors and collaboration load by team or workspace.
5. Lifecycle: Newly active, declining, and inactive spaces or channels.

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 Microsoft Teams data into a database or warehouse?

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

Use Microsoft Graph exports and APIs for durable reporting on teams, channels, chats, messages, meetings, users, and membership; review compliance and retention requirements first.

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 actor, container, event type, thread, created, and response-time fields.

How should you model Microsoft Teams data in Metabase?

Core tables

Modeling advice

• Build a clean collaboration_events model 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 Microsoft Teams metrics should you track in Metabase?

What SQL powers Microsoft Teams dashboards in Metabase?

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

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;

SELECT
  date_trunc('week', thread_created_at) AS week,
  percentile_cont(0.5) WITHIN GROUP (
    ORDER BY EXTRACT(EPOCH FROM (first_reply_at - thread_created_at)) / 3600.0
  ) AS median_first_response_hours
FROM collaboration_threads
WHERE first_reply_at IS NOT NULL
GROUP BY 1
ORDER BY 1;

What are common mistakes when analyzing Microsoft Teams in Metabase?

Related

Related analytics

Related integrations

FAQ