Table of Contents

Automation and Scripting

  • 2026-05-06
  • Peer Grønnerup
Applies to:
  • ❌ Tabular Editor 2
  • ❌ Tabular Editor 3
  • ✔ Tabular Editor CLI
Important

The Tabular Editor CLI is in Limited Public Preview. It is offered for evaluation with a Tabular Editor account; no license is required during preview. Commands, flags, and outputs may change before general availability. The preview build stops functioning after 2026-09-30. We recommend against using the CLI in production CI/CD pipelines during preview. Please refer to our [license agreement](TBD: ADD LINK TO EULA)

The Tabular Editor CLI is designed as a composable building block. Every command supports structured output, disables interactive prompts on demand, and returns predictable exit codes. The same primitives work equally well for shell pipelines, Python scripts, PowerShell automation, and agent-driven workflows.

Structured output

Use --output to switch any command between text (human-readable) and machine-readable formats:

Format Use for Notes
auto (default) General use Text for TTY, JSON when stdout is piped or redirected.
text Force text output Useful when you want text in a CI log.
json Programmatic consumers Always valid JSON to stdout; errors and warnings go to stderr as separate JSON objects.
csv Tabular results (query, bpa, vertipaq) RFC 4180 escaping. 
te ls --output json
te query -q "EVALUATE VALUES('Date'[Year])" --output csv
te bpa run --output json
Tip

When stdout is piped, auto already emits JSON. Explicit --output json is only necessary when you want JSON on a TTY, or when a consumer expects it regardless of how the command is invoked.

Non-interactive mode

Add --non-interactive to any command to disable confirmation prompts, credential picklists, and guided wizards. If the command needs input it cannot resolve from flags, environment, or config, it exits non-zero with an actionable error instead of hanging.

te deploy ./model --non-interactive --force --ci github

Exit codes

Exit Meaning
0 Success.
1 Generic failure — invalid arguments, command failed, validation errors, auth failure, BPA gate failed at severity ≥ error.
2 Used by te diff to indicate models differ (distinct from 0 identical and non-zero errors).

Combine exit codes with --ci <vsts\|github> annotations and --trx <file> to surface rich failure information in CI — see CI/CD Integration.

Errors on stderr

Errors, warnings, and the preview banner are written to stderr; structured data is written to stdout. This means you can pipe JSON safely without it being contaminated by progress indicators or diagnostic messages:

te ls --output json | jq '.[] | .name'
te vertipaq --output json > stats.json

Python

Invoke the CLI from Python with subprocess.run, request JSON, and parse stdout:

import json
import subprocess

def query(server: str, database: str, dax: str) -> list[dict]:
    result = subprocess.run(
        ["te", "query",
         "-s", server,
         "-d", database,
         "-q", dax,
         "--output", "json",
         "--non-interactive"],
        check=True,
        capture_output=True,
        text=True,
    )
    return json.loads(result.stdout)

rows = query("Finance", "Revenue Model", "EVALUATE TOPN(10, 'Sales')")
for row in rows:
    print(row)

Capture structured errors from stderr:

import json
import subprocess

result = subprocess.run(
    ["te", "deploy", "./model",
     "-s", "Finance", "-d", "Revenue",
     "--output", "json", "--non-interactive", "--force"],
    capture_output=True, text=True,
)

if result.returncode != 0:
    try:
        err = json.loads(result.stderr.strip().splitlines()[-1])
        print("Deploy failed:", err.get("error"), "— hint:", err.get("hint"))
    except json.JSONDecodeError:
        print("Deploy failed:\n", result.stderr)

PowerShell

PowerShell handles JSON natively. Because te is a normal console binary, there is no need for the start /wait dance required by TabularEditor.exe:

$rows = te query -s Finance -d Revenue -q "EVALUATE TOPN(10, 'Sales')" --output json --non-interactive
  | ConvertFrom-Json

$rows | Format-Table

# Check exit code after the pipeline
if ($LASTEXITCODE -ne 0) {
    Write-Error "Query failed with exit $LASTEXITCODE"
    exit $LASTEXITCODE
}

Read secrets from the environment rather than passing them as plaintext:

$env:AZURE_CLIENT_ID     = "<app-id>"
$env:AZURE_CLIENT_SECRET = "<secret>"
$env:AZURE_TENANT_ID     = "<tenant>"

te deploy ./model `
  -s my-workspace -d my-model `
  --auth env --non-interactive --force --ci vsts

Bash

Compose commands with pipes and jq. The CLI's text output is colorized for humans, but switching to --output json gives you a clean shape to work with:

# Count measures per table
te ls --type measure --output json \
  | jq -r '.[] | .table' \
  | sort | uniq -c | sort -rn

# Fail the shell script if BPA finds any errors
te bpa run --fail-on error --output json > bpa.json \
  || { echo "BPA gate failed"; jq '.violations' bpa.json; exit 1; }

Composability example

Generating a refresh TMSL script and version-controlling it is three commands:

te connect MyWorkspace MyModel
te refresh --type full --dry-run > refresh.tmsl
cat refresh.tmsl

The resulting TMSL can be reviewed in a pull request, committed, executed by the CLI (te refresh --type full), handed to a DBA, or applied by any XMLA-compatible tool. The CLI becomes a building block rather than a black box.

Useful patterns

  • Idempotent creates and removes. te add Sales/Marker -t Measure -i "0" --if-not-exists --save and te rm Sales/OldMeasure --if-exists --save both exit 0 whether or not the object existed — safe to re-run in CI.
  • Dry-run diffs. te replace is dry-run by default; add --save only when you're satisfied with the preview.
  • Emit TMSL for review. te deploy ./model --xmla deploy.tmsl produces the deployment script without touching the server — useful for DBA review or manual apply.
  • Path-only output. te ls --paths-only and te find --paths-only emit one object path per line, ideal for piping to xargs, te get, or te set. The model-level containers (te ls Measures, te ls Columns) compose well with this for whole-model sweeps.
  • Benchmarking queries. te query --trace --cold --runs 5 runs a DAX query with cold cache, five iterations, and captures FE/SE trace events.
  • Step timings in CI logs. Long-running commands (te deploy, te refresh, te script, te validate) include a durationMs field in JSON output — useful for surfacing per-step timings in pipeline summaries.