present-data

name	present-data
description	Choose the right visualization for a dataset and render it — table, chart, network graph, or globe — then place it well. Use when an agent has structured data to show, for "chart this", "show it as a table", "graph these relationships", or comparisons. Triggers: chart, graph, table, visualize, plot, compare, breakdown, show the data, trend.
license	MIT
compatibility	Requires JarvisVR agent-backend stage runtime. Widget props validated against holo-tools/registry.json.
metadata	{"agent":"stage-agent","category":"stage","version":"1.0","author":"jarvisvr"}
allowed-tools	show_data_table show_chart show_graph show_panel arrange_holograms

Present Data

Pick the visualization that best fits the data's shape and the user's question, build valid props, and place it comfortably.

Choosing the widget

Data shape / intent	Widget	Tool
Rows × columns, look up values	data_table	show_data_table
Trend / comparison of series	chart_3d (line/bar)	show_chart
Distribution / parts of whole	chart_3d (pie/scatter)	show_chart
Entities + relationships	graph_3d	show_graph
Geographic points / flows	volumetric_globe / map_3d	show_globe / show_map
Short prose / KPIs	panel	show_panel

Steps

1. Inspect the data (types, cardinality) and the user's question.
2. Select from the table above; prefer the simplest that answers it.
3. Build props to schema (e.g. chart_3d requires chart_type + series; data_table requires columns + rows).
4. Render, then place via arrange_holograms (charts/tables read best world/surface at ~1.2 m).
5. Narrate the takeaway in agent.speech — the headline, not every number.

Output

chart_3d (show_chart, props per registry.json):

{ "widget_type": "chart_3d",
  "props": { "chart_type": "bar", "title": "Quarterly Revenue",
             "labels": ["Q1","Q2","Q3","Q4"],
             "series": [ { "name": "FY2026", "values": [14,20,15,26], "color": "#4FC3F7" } ],
             "y_axis_label": "Revenue ($M)", "show_legend": true } }

data_table (show_data_table):

{ "widget_type": "data_table",
  "props": { "title": "Devices",
             "columns": [ { "key": "name", "label": "Name", "type": "string" },
                          { "key": "watts", "label": "Watts", "type": "number" } ],
             "rows": [ { "name": "Ceiling Light", "watts": 12 }, { "name": "Heater", "watts": 0 } ],
             "sortable": true } }

Edge cases

• Tiny data (1–2 numbers) → a panel/text_label, not a chart.
• Too many series/rows → aggregate or paginate; keep it legible.
• Categorical vs continuous → bar for categories, line for time series.
• Selection events (select_point, select_row) → drill down or explain.
• Raw source, not data → that's research-agent; you visualize what it returns.