Dataflows Consumption CLI

Update Check — ONCE PER SESSION (mandatory) The first time this skill is used in a session, run the check-updates skill before proceeding.

• GitHub Copilot CLI / VS Code: invoke the check-updates skill.
• Claude Code / Cowork / Cursor / Windsurf / Codex: compare local vs remote package.json version.
• Skip if the check was already performed earlier in this session.

CRITICAL NOTES

1. To find the workspace details (including its ID) from workspace name: list all workspaces and, then, use JMESPath filtering
2. To find a dataflow by name: list all dataflows in the workspace and filter by displayName client-side — there is no server-side name filter
3. getDefinition is a POST, not GET — even though it reads data

SCOPE BOUNDARY — READ-ONLY (mandatory) This skill is strictly read-only. You must never create, update, or delete a dataflow or its definition — that means no Delete Dataflow, no Create Dataflow, no updateDefinition, and no other mutating/destructive call (e.g. az rest --method delete/put/patch against a dataflow, or a POST that creates/overwrites). The only permitted POSTs are the explicitly read-side getDefinition and executeQuery operations documented below. If the user asks to delete, create, modify, or persist a dataflow, explicitly refuse the mutation in the final response and name dataflows-authoring-cli only as a future handoff. Do not invoke that skill, perform discovery in preparation for the write, or continue the write workflow in the same turn. A mutation may begin only after the user explicitly confirms a separate authoring request.

dataflows-consumption-cli — Dataflows Gen2 Consumption via CLI

Table of Contents

Tool Stack

Agent check — verify before first operation:

az account show >/dev/null 2>&1 || echo "RUN: az login"
command -v jq >/dev/null 2>&1 || echo "INSTALL: apt-get install jq OR brew install jq"

Connection

Resolve Workspace ID and Dataflow ID

Per COMMON-CLI.md Finding Workspaces and Items in Fabric:

# Find workspace ID by name
WS_ID=$(az rest --method get \
  --resource "https://api.fabric.microsoft.com" \
  --url "https://api.fabric.microsoft.com/v1/workspaces" \
  --query "value[?displayName=='My Workspace'].id" --output tsv)

# Find dataflow ID by name within workspace
DF_ID=$(az rest --method get \
  --resource "https://api.fabric.microsoft.com" \
  --url "https://api.fabric.microsoft.com/v1/workspaces/$WS_ID/dataflows" \
  --query "value[?displayName=='Sales Data Pipeline'].id" --output tsv)

Reusable Connection Variables

# Set once at script top
WS_ID="<workspaceId>"
DF_ID="<dataflowId>"
API="https://api.fabric.microsoft.com/v1"
AZ="az rest --resource https://api.fabric.microsoft.com"

Agentic Exploration ("Chat With My Dataflows")

Discovery Sequence

Run these in order to fully explore a workspace's dataflows. See references/discovery-queries.md for extended patterns.

# 1. List workspaces → find target
az rest --method get --resource "https://api.fabric.microsoft.com" \
  --url "$API/workspaces" --query "value[].{name:displayName, id:id}" -o table

# 2. List dataflows → enumerate all
az rest --method get --resource "https://api.fabric.microsoft.com" \
  --url "$API/workspaces/$WS_ID/dataflows" \
  --query "value[].{name:displayName, id:id, desc:description}" -o table

# 3. Get dataflow properties
az rest --method get --resource "https://api.fabric.microsoft.com" \
  --url "$API/workspaces/$WS_ID/dataflows/$DF_ID"

# 4. Discover parameters
#    Note: the /parameters endpoint returns DataflowNotParametricError (an HTTP 4xx)
#    for a non-parametric dataflow (no Power Query parameters). Treat that as
#    "this dataflow has no parameters" and report it plainly — do NOT surface the
#    raw error. Optionally confirm by checking mashup.pq for `IsParameterQuery`.
az rest --method get --resource "https://api.fabric.microsoft.com" \
  --url "$API/workspaces/$WS_ID/dataflows/$DF_ID/parameters" \
  --query "value[].{name:name, type:type, required:isRequired, default:defaultValue}" -o table

# 5. Get definition → decode mashup.pq
RESPONSE=$(az rest --method post --resource "https://api.fabric.microsoft.com" \
  --url "$API/workspaces/$WS_ID/dataflows/$DF_ID/getDefinition")
echo "$RESPONSE" | jq -r '.definition.parts[] | select(.path=="mashup.pq") | .payload' | base64 --decode

# 6. Check job history
az rest --method get --resource "https://api.fabric.microsoft.com" \
  --url "$API/workspaces/$WS_ID/dataflows/$DF_ID/jobs/instances" \
  --query "value[].{status:status, type:invokeType, start:startTimeUtc, end:endTimeUtc, error:failureReason}" -o table

Agentic Workflow

1. Discover → Run Steps 1–3 to list and identify dataflows.
2. Parameters → Step 4 to understand inputs and defaults.
3. Definition → Step 5 to inspect M queries, connections, staging config.
4. Monitor → Step 6 for refresh history and error patterns.
5. Iterate → Drill into specific queries or connection details.
6. Present → Summarize findings or generate a reusable script (see script-templates.md).

Gotchas, Rules, Troubleshooting

For full platform gotchas: DATAFLOWS-CONSUMPTION-CORE.md Gotchas and Troubleshooting Reference and COMMON-CLI.md Gotchas & Troubleshooting (CLI-Specific).

MUST DO

• Always az login first — az rest uses the active session. No session → cryptic failure.
• Always --resource "https://api.fabric.microsoft.com" — wrong audience = 401.
• Handle pagination — repeat requests with continuationToken until absent/null.
• Handle LRO for getDefinition — may return 202 Accepted with Location header; poll until complete.
• Decode base64 before inspecting — definition parts are base64-encoded.
• Use POST for getDefinition — it is NOT a GET endpoint.

AVOID

• Hardcoded GUIDs — always discover via list-then-filter pattern.
• Assuming getDefinition is GET — it is POST (common mistake).
• Ignoring pagination — list endpoints may return partial results.
• Polling too aggressively — respect Retry-After headers on 429s.
• Expecting getDefinition with Viewer role — requires Read+Write (Contributor+).

PREFER

• az rest over raw curl — handles auth automatically.
• List-then-filter pattern — no server-side name filter for dataflows.
• Exponential backoff for job polling — 5s → 10s → 20s → 30s cap.
• jq for response parsing — cleaner than shell string manipulation.
• JMESPath --query for simple field extraction directly in az rest.
• Env vars (WS_ID, DF_ID, API) for script reuse.

TROUBLESHOOTING

Examples

Example 1: List All Dataflows in a Workspace

Prompt: "List all dataflows in this workspace."

Agent response:

az rest --method get \
  --url "https://api.fabric.microsoft.com/v1/workspaces/${WS_ID}/dataflows" \
  --resource "https://api.fabric.microsoft.com" \
  --query "value[].{Name:displayName, Id:id, Type:type}" -o table

Example 2: Decode a Dataflow Definition

Prompt: "Show me the Power Query M code inside this dataflow."

Agent response:

# Step 1: Request definition (POST returns 202 with Location header).
# az rest cannot expose response headers, so capture Location via curl + az-acquired token.
TOKEN=$(az account get-access-token --resource "https://api.fabric.microsoft.com" --query accessToken -o tsv)
LOCATION=$(curl -sS -X POST \
  -H "Authorization: Bearer $TOKEN" -H "Content-Length: 0" \
  "https://api.fabric.microsoft.com/v1/workspaces/${WS_ID}/dataflows/${DF_ID}/getDefinition" \
  -o /dev/null -D - | tr -d '\r' | grep -i "^location:" | awk '{print $2}')

# Step 2: Poll until definition is ready
DEF=$(az rest --method get --url "${LOCATION}" \
  --resource "https://api.fabric.microsoft.com")

# Step 3: Decode mashup.pq to see the Power Query M code
echo "$DEF" | python3 -c "
import json, base64, sys
parts = json.load(sys.stdin)['definition']['parts']
for p in parts:
    if p['path'] == 'mashup.pq':
        print(base64.b64decode(p['payload']).decode('utf-8'))
"

Example 3: Check Refresh Job History

Prompt: "Show me the recent refresh history for this dataflow."

Agent response:

# Get recent job instances for a dataflow
az rest --method get \
  --url "https://api.fabric.microsoft.com/v1/workspaces/${WS_ID}/dataflows/${DF_ID}/jobs/instances?limit=5" \
  --resource "https://api.fabric.microsoft.com" \
  --query "value[].{Status:status, Start:startTimeUtc, End:endTimeUtc, Id:id}" -o table

Example 4: Discover Parameters from Definition

Prompt: "What parameters does this dataflow accept?"

Agent response:

# After decoding the definition (see Example 2), extract parameters:
echo "$DEF" | python3 -c "
import json, base64, sys
parts = json.load(sys.stdin)['definition']['parts']
for p in parts:
    if p['path'] == 'queryMetadata.json':
        meta = json.loads(base64.b64decode(p['payload']).decode('utf-8'))
        for qname, qmeta in meta.get('queriesMetadata', {}).items():
            if qmeta.get('queryGroupId') == 'parameters' or 'IsParameterQuery' in str(qmeta):
                print(f'Parameter: {qname}')
"

Query Evaluation

Execute an individual query from a dataflow and inspect results. Responses are a raw Apache Arrow IPC stream with Content-Type: application/vnd.apache.arrow.stream — not a JSON envelope. The first four bytes of a valid stream are the IPC continuation marker ff ff ff ff. Parse with pyarrow.ipc.open_stream().

Wire format: executeQuery returns the raw Apache Arrow IPC byte stream (Content-Type: application/vnd.apache.arrow.stream) — not JSON. Don't try to parse it with jq — there is no JSON envelope to extract. Use --output-file to save the bytes and parse as Arrow (see Examples 5–7).

Failures return HTTP 200: executeQuery returns 200 OK with application/vnd.apache.arrow.stream even when the underlying source query fails (Kusto SEM0100, T-SQL syntax error, missing column, etc.). The error is embedded inside the stream's PQ Arrow Metadata section as {"Error":"..."} — see dataflows-authoring-cli § mashup-preview.md → Detecting failures inside the Arrow body for detector snippets. Naive HTTP-status checks will treat failures as success.

Intent split (canonical executeQuery reference is mashup-preview.md): the same executeQuery endpoint serves two distinct intents. This skill covers the consumption intents:

• (a) Execute a persisted query — body {"QueryName":"<saved-shared>"} only (no customMashupDocument).
• (b) Ad-hoc read-only customMashupDocument — preview a candidate section Section1; ... document with no intent to persist via updateDefinition (Example 7).

If you intend to persist the M, use dataflows-authoring-cli § Workflow C (Preview-Driven Authoring Loop) — it adds the bootstrap-bind rule (chicken-and-egg connection binding for new credentialed dataflows), auto-wrap rule, hard-avoid for unbounded preview, and the post-preview persistence steps.

Auto-wrap caveat: The Fabric REST API expects customMashupDocument to be a complete section Section1; ... shared X = ...; document. Raw let ... in ... expressions are not auto-wrapped server-side — send a full section document and ensure the QueryName request field matches a shared member declared inside it.

Body shape: send a flat object with a top-level QueryName (field name is case-insensitive on the wire; PascalCase canonical). The {"queries":[{...}]} array shape always returns 400 DataflowExecuteQueryError: Invalid query name regardless of inner casing. A wrong QueryName value returns QueryNotFound (different error code). See dataflows-authoring-cli § mashup-preview.md → Request body.

TimedOut recovery for heavy persisted queries: if the persisted shared <Q> member is too expensive for the server-side executeQuery budget (~90s observed; surfaces as 400 DataflowExecuteQueryError → InternalErrorCode: EvaluationError, Message: Evaluation result error code: TimedOut), do not retry the same body — it is deterministic. Instead, re-issue with customMashupDocument set to the full persisted mashup.pq (decoded via Example 2) plus an appended probe shared __PreviewCap = Table.FirstN(<Q>, 10); and QueryName: "__PreviewCap". The cap pushes down for most connectors (Kusto, SQL, OData, web tables) so the source returns only the sample. See dataflows-authoring-cli § mashup-preview.md → Observed InternalErrorCode values.

Prerequisites

• Dataflow must exist (verify via Example 1 above)
• Query name must exist in the dataflow (verify in mashup.pq via Example 2)
• User must have Contributor role or higher (read+write) on the dataflow
• Optional: Query parameters and custom M code modifications

Example 5: Execute a Query and Save Results

Prompt: "Execute the SalesData query in this dataflow and save the results."

Agent response:

# Step 1: Identify the query to execute (must be a `shared` member of the dataflow's mashup)
QUERY_NAME="SalesData"  # Replace with your query name

# Step 2: Build the request body. To execute the persisted query as-is, send ONLY
#   QueryName — omit customMashupDocument. (Do NOT pass a self-referential
#   `shared X = let Source = #shared[X] in Source` — that recurses on itself
#   inside the candidate document.) For custom M, see Example 7.
jq -n --arg q "$QUERY_NAME" '{QueryName: $q}' > req.json

# Step 3: Execute the query (raw Apache Arrow IPC stream is written directly to disk)
az rest --method post \
  --url "https://api.fabric.microsoft.com/v1/workspaces/${WS_ID}/dataflows/${DF_ID}/executeQuery" \
  --resource "https://api.fabric.microsoft.com" \
  --body "@req.json" \
  --output-file "${QUERY_NAME}_results.arrow"

# Step 4: Check for embedded source errors (HTTP 200 does NOT mean success)
if grep -q '"Error":"' "${QUERY_NAME}_results.arrow"; then
  echo "Query execution failed (embedded in Arrow body):"
  python3 -c "import re,sys; raw=open(sys.argv[1],'rb').read().decode('utf-8','replace'); m=re.search(r'\\{\"Error\":\"[^\"]+\"\\}', raw); print(m.group(0) if m else '(marker present, JSON not parsed)')" "${QUERY_NAME}_results.arrow"
  exit 1
fi

echo "Query results saved to ${QUERY_NAME}_results.arrow"

Why --output-file? Without it, az rest dumps raw bytes to stdout and prints the warning Not a json response, outputting to stdout. For binary data suggest use "--output-file" to write to a file. Always pass --output-file for executeQuery so the Arrow IPC stream is captured cleanly. The jq -r '.data' | base64 --decode pattern from earlier docs is incorrect for az rest — there is no JSON envelope to extract.

Example 5b: Render Query Results as a Markdown Table

Prompt: "Show me the first 10 rows of those results."

Agent response — read the saved Arrow file and render head(N) as a markdown table in chat. Always do this after executeQuery (default N=10). The visual preview catches silent-success bugs that the embedded-error detector misses: filter dropped all rows, wrong column referenced, wrong cast, off-by-one filter, join key typo.

N=${N:-10}; ARROW_FILE="${QUERY_NAME}_results.arrow"
python3 - <<EOF
import json, sys, pyarrow as pa, pyarrow.ipc as ipc

with open("$ARROW_FILE", "rb") as f:
    try:
        reader = ipc.open_stream(f)
    except pa.lib.ArrowInvalid:
        f.seek(0); reader = ipc.open_file(f)
    # Surface any embedded engine error before rendering (mirrors mashup-preview.md § Error handling — A)
    for v in (reader.schema.metadata or {}).values():
        s = v.decode("utf-8", errors="replace")
        if '"Error"' in s:
            try:
                msg = json.loads(s)["Error"]
            except (ValueError, KeyError):
                msg = s[:200]
            sys.exit(f"Preview failed: {msg}")
    table = reader.read_all()

rows, cols = table.num_rows, table.num_columns
print(f"**{rows} rows × {cols} columns** · {', '.join(table.schema.names)}\n")
if rows == 0:
    sys.exit(0)

# Slice at the Arrow level before converting — avoids materialising the whole result.
df = table.slice(0, $N).to_pandas().copy()
# Truncate long string cells so the chat table stays readable.
for c in df.select_dtypes(include=["object", "string"]).columns:
    df[c] = df[c].astype(str).str.slice(0, 50)
try:
    print(df.to_markdown(index=False))           # requires the `tabulate` package
except ImportError:
    print(df.to_string(index=False))             # fallback: fixed-width text
EOF

Optional dep: pandas.DataFrame.to_markdown() requires the tabulate package — install once via your environment's standard Python tooling, the same way you installed pyarrow / pandas for Example 6. If tabulate is absent, the snippet falls back to fixed-width to_string() so rendering still works.

When to skip rendering: render head(N) by default. Skip only when (a) the user explicitly asked for the raw Arrow file with no preview, (b) the result row count exceeds ~1000 — in that case still render head(N) and add a one-line note about the total row count, or (c) the result is being streamed straight into another tool that will consume the Arrow file directly. In every other case, render — the saved file alone is not visible to the user in chat.

Example 6: Convert Arrow Results to CSV

Prompt: "Convert my Arrow query results to CSV so I can open them in Excel."

Agent response:

Prerequisite (one-time client-side setup): This example uses pyarrow to decode the Arrow IPC stream and pandas to write CSV. Install them once via your environment's standard Python tooling. These are local client dependencies, not part of the Fabric recipe.

python3 <<EOF
import pyarrow as pa
import pandas as pd
import sys

ARROW_FILE = "${QUERY_NAME}_results.arrow"
CSV_FILE = "${QUERY_NAME}_results.csv"

# Open the Arrow IPC stream directly (raw on the wire, no envelope)
with open(ARROW_FILE, "rb") as f:
    reader = pa.ipc.open_stream(f)

    # Defensive: surface any error embedded in the stream's PQ Arrow Metadata
    md = reader.schema.metadata or {}
    for k, v in md.items():
        s = v.decode("utf-8", errors="replace")
        if '"Error"' in s:
            print(f"Preview failed: {s}", file=sys.stderr)
            sys.exit(1)

    table = reader.read_all()

# Convert to pandas and export as CSV
df = table.to_pandas()
df.to_csv(CSV_FILE, index=False)

print(f"Converted {len(df)} rows to CSV")
print("Columns:", list(df.columns))
EOF

Example 7: Query with Custom M Code

Prompt: "Run a one-off ad-hoc M query against this dataflow without saving it."

Intent: ad-hoc read-only execution. The customMashupDocument is not persisted. If you intend to save the M via updateDefinition, use dataflows-authoring-cli § Workflow C instead — it adds bootstrap-bind, auto-wrap, and post-preview persistence rules.

Agent response:

# Execute a query with custom M code (e.g., filter, aggregate, transform).
# The customMashupDocument must be a complete `section` document; az rest does NOT auto-wrap raw expressions.
CUSTOM_M='section Section1;

shared CustomQuery = let
    Source = Table.FromRecords({[id=1, name="Alice"], [id=2, name="Bob"]}),
    Filtered = Table.SelectRows(Source, each [id] > 0)
in
    Filtered;'

jq -n --arg m "$CUSTOM_M" '{QueryName: "CustomQuery", customMashupDocument: $m}' > req.json

az rest --method post \
  --url "https://api.fabric.microsoft.com/v1/workspaces/${WS_ID}/dataflows/${DF_ID}/executeQuery" \
  --resource "https://api.fabric.microsoft.com" \
  --body "@req.json" \
  --output-file custom_results.arrow

# Always check for embedded errors before treating the file as a success
if grep -q '"Error":"' custom_results.arrow; then
    echo "Custom query failed; inspect custom_results.arrow for the embedded {\"Error\":...} block."
    exit 1
fi

Output Expectations

When this skill completes a task, the agent should return: