dt-app-notebooks

Dynatrace Notebook Skill

Overview

Dynatrace notebooks are JSON documents stored in the Document Store containing an ordered array of sections — markdown blocks for narrative and dql blocks for DQL queries with visualizations. Sections render top-to-bottom in array order.

When to use: Creating, modifying, querying, or analyzing notebooks.

Notebook JSON Structure

{
  "name": "My Notebook",
  "type": "notebook",

• Sections render in array order.
• Section types: markdown, dql. (function exists but is rare.)
• Use string-int IDs ("1", "2", …); UUIDs are also accepted.
• content.defaultTimeframe sets the default timeframe; each section can override via section.state.input.timeframe. Hardcoded time filters in DQL are allowed.

Optional content properties: defaultSegments.

Reading & Analyzing

Fetch full content with dtctl get notebook <id> -o json --plain (describe returns metadata only), then inspect the JSON to discover its available properties. Carefully read references/analyzing.md before analyzing.

Create/Update Workflow (Mandatory Order)

Carefully follow the workflow described in references/create-update.md.

Key rules:

• Load domain skills BEFORE generating queries — do not invent DQL.
• Validate ALL section queries before adding to the notebook.
• Set name before deploying.
• Prefer autoSelectVisualization: true in visualizationSettings unless the user requested a specific visualization type — when false, state.visualization must be set explicitly.
• Updating — ALWAYS download first: dtctl get notebook <id> -o json --plain > notebook.json, modify, then deploy the downloaded file. Never reconstruct JSON from scratch or inject an id manually — both silently overwrite UI edits the user made since last deployment.
• Deploy with dtctl apply — validation runs automatically, and the local file is deleted on success.

Visualization Types

Notebooks support a subset of Dynatrace visualizations:

• Time-series (require timeseries/makeTimeseries): lineChart, areaChart, barChart, bandChart
• Categorical (summarize ... by:{field}): categoricalBarChart, pieChart, donutChart
• Single value / gauge / meter: singleValue, meterBar, gauge
• Tabular (any data shape): table, raw, recordView
• Distribution/status: histogram, honeycomb
• Geographic maps: choropleth, dotMap, connectionMap, bubbleMap
• Matrix/correlation: heatmap, scatterplot

Required field types per visualization: references/sections.md.

References