Flint Chart Author

flint-chart: authoring and using a chart spec

What you produce (and what you do NOT)

Your output is the spec: the chart_spec and semantic_types of a ChartAssemblyInput. You reference data columns by name. The host passes the resulting input to assembleVegaLite, assembleECharts, or assembleChartjs to get a backend spec.

You write the input spec, not the output spec. And critically:

• DO emit chart_spec (chart type, channel→field mapping, properties) and semantic_types (field → semantic type).
• Reference columns by name. How data itself gets bound depends on the situation — a URL, a host-side variable, or embedded rows (see "How data gets bound"). Embedding is fine for small tables; just don't re-serialize a large dataset by hand, since that risks truncation and silent value corruption and wastes tokens.
• Transform data before Flint. If the requested chart needs aggregation, filtering, joins, pivots, derived columns, or long/wide reshaping beyond Flint's built-in static-series fold, use a coding, notebook, SQL, or data tool first. Then author the Flint spec against the transformed table.
• Style after Flint, only when needed. Author structure in Flint. For a presentation tweak Flint does not express (a reference line, annotation, or shaded band), use the Vega-Lite escape hatch — see "Post-Flint style customization". Never feed edited Vega-Lite JSON back to render_chart.

When the user wants more than a spec

First decide which workflow the user is asking for:

• Spec authoring only: return a ChartAssemblyInput or its semantic_types + chart_spec pieces. Do not install packages or write renderer code unless asked.
• MCP chart output: if Flint MCP tools are available, default to create_chart_view whenever the user asks to see a chart — it opens an interactive, live-rendered view with a customization panel, and it validates the spec for you. Only fall back to render_chart (PNG/SVG) when the host has no App UI support or the user explicitly wants a static image. Use validate_chart to check a spec without rendering, compile_chart when the user wants backend-native JSON, and list_chart_types when you need the supported chart catalog.
• Project integration, only when the user asks for code: add Flint to an app, notebook, script, or agentic product, install/import the library, and call an assembler in code. Keep the same ChartAssemblyInput contract, then let the host render the backend result.

For MCP clients, the server can run with npx:

npx -y flint-chart-mcp

For JavaScript or TypeScript projects, install Flint first and add only the renderer peer dependencies needed by the backend you will render:

npm install flint-chart
npm install vega vega-lite vega-embed  # browser Vega-Lite rendering
npm install echarts                    # ECharts rendering
npm install chart.js                   # Chart.js rendering

Then compile with the requested backend:

import { assembleChartjs, assembleECharts, assembleVegaLite } from 'flint-chart';

const vegaLiteSpec = assembleVegaLite(input);
const echartsOption = assembleECharts(input);
const chartjsConfig = assembleChartjs(input);

Python support is planned for a later release. Until the PyPI package is published, use the npm package or MCP server for released workflows.

interface ChartAssemblyInput {
  // Bound by the HOST or by you, depending on the situation (see below).
  data: { values: any[] } | { url: string };
  semantic_types?: Record<string, string>;   // field → semantic type  ← you write this
  chart_spec: {                               //                        ← you write this
    chartType: string;                        // e.g. "Scatter Plot"
    encodings: Record<string, EncodingValue>; // channel → { field, ... } (or array)
    baseSize?: { width: number; height: number };    // target layout size, default 400×320
    canvasSize?: { width: number; height: number };  // optional hard ceiling on stretch
    chartProperties?: Record<string, any>;    // per-chart tuning (optional)
  };
  options?: Record<string, any>;              // global layout options (rarely needed)
}

How data gets bound

Use the binding mode that matches the runtime. Do not mix them.

1. Direct MCP rendering: embed rows. When calling render_chart, compile_chart, or validate_chart, the tool arguments are JSON. If the data is small or already transformed by another tool, pass it as data: { values: [...] }. Do not pass runtime variable names in MCP tool calls — the MCP server cannot see your local variables.
2. Direct MCP rendering: reference a local file. The flint-chart-mcp server can load data: { url: "..." } from local .json, .csv, or .tsv files. By default any local file the agent can name is readable (relative paths resolve against the working directory); a hardened deployment may reject local file references entirely via --disable-file-reference (or FLINT_MCP_DISABLE_FILE_REFERENCE), in which case pass rows inline with data.values. Remote URL fetching is disabled. If the data must be transformed first, use a coding/data tool to write a small prepared file, then reference that file.
3. Generated application or notebook code: bind runtime variables. If the user asks you to add Flint to code, write normal data-loading code first and pass a real runtime value, e.g. data: { values: rows }, to assembleVegaLite, assembleECharts, or assembleChartjs. This variable pattern is for generated code, not for MCP tool calls.

For spec-only answers, return the semantic_types and chart_spec pieces and state how the host should bind data. In the worked examples below, data is shown as { values: [] } to signal "host binds this" — focus on chart_spec and semantic_types.

Data transformation before charting

Flint is a chart compiler, not a data-wrangling layer. If the chart needs grouped totals, time buckets, filters, joins, pivots, derived ratios, or a long-form table, transform the data first with a host tool, then bind the prepared table (see "How data gets bound"). Pick semantic types and channels for the transformed columns, not for columns that no longer exist.

Sanity-read the values first — don't chart blind. Inspect the actual data with your data tool (distinct values per category column, min/max per measure), not just the column names, and watch for:

• Embedded totals. A category column may mix an aggregate level with its parts (e.g. all alongside cage-free/caged, or a Total region). Charting the total with its parts double-counts and flattens the parts — keep one or the other on a stacked/grouped/colored channel, not both.
• Units. Check whether a rate is a fraction (0–1) or already a percent (0–100) before tagging it Percentage; don't scale twice.
• One real entity. If your breakdown column has a single distinct value, the per-group chart collapses to one mark — the intended breakdown is likely a different column.

Post-Flint style customization

Stay at the Flint level for structure (data, chart type, channels, transforms, sizing, properties) — Flint specs stay portable and regenerate safely. Drop to backend JSON only after a valid Flint chart exists, and only for a narrow presentation change Flint does not expose (exact axis/legend/mark styling, titles, annotations, reference lines, layout polish). Never use it to change the data, chart type, field mappings, or transforms — fix those upstream.

For a Vega-Lite-specific style tweak:

1. Author and validate the Flint ChartAssemblyInput.
2. Render or inspect the Flint chart first, when possible.
3. Call compile_chart with backend: "vegalite".
4. Make the smallest necessary style/presentation edit to the returned Vega-Lite spec.
5. Render the edited spec in the host environment with a Vega-Lite renderer.

This edited Vega-Lite spec is no longer a portable Flint spec. Do not send it to render_chart; use render_chart only for Flint ChartAssemblyInput.

Step 1 — pick chartType

Use one of the registered names exactly. Vega-Lite is the default and broadest backend; the table below lists each Vega-Lite chart type, the channels it accepts, and its tuning properties (see "Chart-level properties"). Required channels are noted.

Donut chart: use "Pie Chart" with chartProperties.innerRadius > 0.

Choosing a bar chart (most common mix-up). All three take one discrete category on x (or y) plus one measure. They differ in how a second category is shown — and each reads that second category from a different channel:

• "Bar Chart" — use for a single series. When multiple rows share an x, a second category on color produces stacked segments. For side-by-side bars, use "Grouped Bar Chart" with the second category on group.
• "Stacked Bar Chart" — second category on color, drawn as stacked segments within each bar (totals matter). Tune with stackMode (stacked / normalize / layered).
• "Grouped Bar Chart" — second category on the group channel, drawn as side-by-side (dodged) bars within each x cluster (compare values directly). Put the clustering category on group, not color.

Rule of thumb: comparing parts-to-whole → Stacked; comparing values side-by-side → Grouped (use group); single series → Bar.

Waterfall color is a special "Type" column, not a free category. On a "Waterfall Chart" the color channel is reserved for a type field whose values are literally start, delta, and end — it drives which bars anchor to zero, not an arbitrary grouping. Do not bind color to an Increase/Decrease (or up/down, gain/loss) category: the up/down direction is already derived from the sign of the y value and colored automatically (green up / red down). For the common case, omit color entirely and let Flint infer the start/delta/end and per-bar sign coloring. To force which bars are anchored totals, use the totals property (first/last/both), not a color field. Only bind color when you genuinely have a start/delta/end type column.

Backend coverage. Vega-Lite supports all of the above. Other backends support a subset (verify if targeting a non-VL backend):

• ECharts adds: "Calendar Heatmap", "Gauge", "Funnel", "Treemap", "Sunburst", "Sankey", "Parallel Coordinates", "Graph", "Tree".
• Chart.js supports: Scatter, Bubble, Bar, Grouped Bar, Stacked Bar, Combo, Line, Bump, Area, Range Area, Pie, Doughnut, Histogram, Radar, Rose, Slope, Connected Scatter.

You do not need to call the library or inspect its source to author the input — pick from this table.

Step 2 — map fields to channels

Each channel maps to an encoding object { field, ... } (or a bare string shorthand, expanded to { field: "<string>" }):

"encodings": {
  "x": { "field": "weight" },
  "y": "mpg",
  "color": { "field": "origin" }
}

Encoding object fields (all optional except field):

You usually don't need type, aggregate, or sortOrder — they're inferred from the semantic type. Set them only with specific intent.

Multi-series (wide → long). To plot several measure columns as series, pass an array on x or y (only those two channels). The library folds them into long form and synthesizes a series/legend field:

"encodings": { "x": { "field": "month" }, "y": ["sales", "profit"] }

All array fields must be quantitative, and you cannot also bind color when using the array form (the fold owns the color/legend). This is the only built-in reshape — there is no transforms/fold property. For any other shape (long↔wide, an aggregate the encodings can't express, a derived column, a pivot, a join), reshape the data first with a host tool — pandas/polars, Arquero/Array.map/SQL, or a data/MCP tool — and pass the result as data.values. If you have no way to transform, surface the gap to the developer rather than inventing a transform property that does not exist.

Step 3 — annotate with semantic types

This is the most important step. Semantic types drive all downstream decisions — formatting, zero baseline, color scheme, scale direction, and more. Pick the most specific type for each field. Full registered set:

What choosing well gets you (automatically):

• Price / Amount → currency formatting, zero baseline, sequential color
• Temperature → diverging color scheme, no forced zero baseline
• Correlation → fixed [-1, 1] diverging domain
• Rank → reversed axis (1 on top), discrete color
• Date / DateTime → temporal axis with auto-granularity formatting
• Percentage → percent formatting, 0–100 domain awareness

If you don't know, use Quantity for numbers, Category for strings, Date/DateTime for date-shaped values. Do not invent type names.

Chart-level properties (chartProperties)

chartProperties is an optional per-chart tuning map. Set a property only when the user asks for that behavior — defaults are sensible. These are design choices, not styling overrides (colors/fonts/ticks are still derived). Values are clamped to the ranges shown.

Cross-cutting properties (apply to position/faceted charts when relevant; set only to force non-default behavior):

• independentYAxis (boolean) — faceted charts: give each panel its own y-scale.
• logScale_x / logScale_y (boolean) — force a logarithmic axis.
• includeZero_x / includeZero_y (boolean) — force the axis to include 0.
• xAxisType / yAxisType (temporal | nominal) — force a temporal field to render as discrete bands (or vice-versa).

Parameter overrides — when to reach for them

Overrides exist, but prefer letting semantic types drive decisions. Reach for an override only when the user's intent genuinely conflicts with the default:

• Force an aggregation: encodings.y = { field: "sales", aggregate: "sum" }.
• Sort a category axis by its measure: encodings.x = { field: "name", sortBy: "y", sortOrder: "descending" }.
• Pick a color scheme: encodings.color = { field: "region", scheme: "tableau10" }.
• Override an inferred type: encodings.x = { field: "year", type: "ordinal" } (e.g. treat a year as discrete bands).
• Resize the chart: Flint sizes from two numbers — baseSize (the target it aims for, default 400×320) and canvasSize (a hard ceiling it may never exceed). With dense data the chart stretches from base toward the ceiling.
  • Want a comfortable size that may grow for dense data → set chart_spec.baseSize = { width, height }.
  • Want a fixed slot it must fit inside → set chart_spec.canvasSize = { width, height } alone; the chart fills it and shrinks to fit, never overflowing. What you ask for is what you get.
  • Both → aims for baseSize, grows toward canvasSize, never beyond.
• Force log / zero baseline: the logScale_* / includeZero_* chart properties above.

Global layout tuning lives in the top-level options object (e.g. addTooltips, band padding, facet sizing). It is rarely needed for authoring — omit it unless asked.

Worked examples

In each example data is a placeholder — the host binds real rows or a URL. You author only chart_spec and semantic_types.

Scatter plot

User: "Plot car weight vs fuel economy, colored by origin."

{
  "data": { "values": [] },
  "semantic_types": {
    "weight": "Quantity",
    "mpg": "Quantity",
    "origin": "Country"
  },
  "chart_spec": {
    "chartType": "Scatter Plot",
    "encodings": {
      "x": { "field": "weight" },
      "y": { "field": "mpg" },
      "color": { "field": "origin" }
    },
    "baseSize": { "width": 400, "height": 300 }
  }
}

Revenue bar chart with facets, sorted by value

User: "Show revenue by product line, biggest first, one panel per region."

{
  "data": { "values": [] },
  "semantic_types": {
    "product_line": "Category",
    "revenue": "Amount",
    "region": "Region"
  },
  "chart_spec": {
    "chartType": "Bar Chart",
    "encodings": {
      "x": { "field": "product_line", "sortBy": "y", "sortOrder": "descending" },
      "y": { "field": "revenue" },
      "column": { "field": "region" }
    }
  }
}

Time series, multiple series (wide → long via array)

User: "Line chart of monthly sales and profit."

{
  "data": { "values": [] },
  "semantic_types": {
    "month": "YearMonth",
    "sales": "Amount",
    "profit": "Profit"
  },
  "chart_spec": {
    "chartType": "Line Chart",
    "encodings": {
      "x": { "field": "month" },
      "y": ["sales", "profit"]
    },
    "chartProperties": { "interpolate": "monotone", "showPoints": true }
  }
}

Donut chart (Pie + innerRadius), value on size

User: "Show market share by vendor as a donut."

Pie/donut maps the slice value to size (rendered as angle) and the category to color. Data is already long (one row per vendor).

{
  "data": { "values": [] },
  "semantic_types": {
    "vendor": "Category",
    "share": "Percentage"
  },
  "chart_spec": {
    "chartType": "Pie Chart",
    "encodings": {
      "size": { "field": "share" },
      "color": { "field": "vendor" }
    },
    "chartProperties": { "innerRadius": 60 }
  }
}

Bullet chart (KPI vs target)

User: "Show each rep's sales against their quota."

{
  "data": { "values": [] },
  "semantic_types": {
    "rep": "Name",
    "sales": "Amount",
    "quota": "Amount"
  },
  "chart_spec": {
    "chartType": "Bullet Chart",
    "encodings": {
      "y": { "field": "rep" },
      "x": { "field": "sales" },
      "goal": { "field": "quota" }
    }
  }
}

What you should NOT do

• Don't re-emit the data. Reference columns by name; let the host bind data (url, variable, or small literal). Never paste large datasets.
• Don't write backend specs directly — write the ChartAssemblyInput, then call the assembler. That's the whole point.
• Don't invent transforms. The only built-in reshape is the array form on x/y. If the data shape is wrong for the chart, say so and ask the host to reshape it.
• Don't invent field names. Reference only columns that exist, spelled exactly. If the data is the wrong shape for the chart, reshape it upstream rather than guessing column names that aren't there.
• Don't set type/aggregate/sortOrder unless intent conflicts with the default.
• Don't pass colors, font sizes, axis tick counts — the compiler derives these. Users fine-tune the output spec.
• Don't invent semantic type names. If none fit, use the family default (Quantity, Category, Date).
• Don't call the library to discover channels/types — this document is the authoring reference.

Validation checklist

Before returning, verify:

1. chartType is an exact registered name supported by the target backend.
2. Every field referenced in encodings is a real column name.
3. Every encoded field has an entry in semantic_types (specific type).
4. Required channels for the chart type are present (e.g. Bullet→goal, Candlestick→open/high/low/close, Pie→size+color).
5. Any chartProperties keys are valid for that chart type and in range.
6. You did not inline large data or hand-tune derived styling.
7. The data carries no embedded total/subtotal level (e.g. an all / total row) mixed with its components on a stacked, grouped, or colored channel.