About microcharts
An MCP server and Vercel AI SDK toolset for selecting, configuring, and rendering word-sized React charts. It returns self-contained SVG with generated alt text, so charts can be shown without a React-capable chat surface.
Key features
- 01Find charts from plain-language questions
- 02Get chart setup details and sample props
- 03Render self-contained SVG with alt text
- 04Expose chart catalog and setup prompt resources
- 05Reject invalid or oversized data
Use cases
- 01Choosing a compact chart for a question
- 02Adding a chart to a codebase with ready-to-use examples
- 03Displaying a rendered chart in a chat surface without React
MCP support
Exposes three tools to find, inspect, and render microcharts, plus catalog and setup-prompt resources. MCP clients launch the stdio server on demand.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
3Saved tool list · updated 2w ago
| Parameter | Type | Required | Description |
|---|---|---|---|
| limit | integer | No | Max results (default 6). |
| question | string | Yes | What the data needs to show, in plain language. |
| dataShape | string | No | Optional filter, e.g. "number[]". |
Details
From the project README@microcharts/mcp
An MCP server and Vercel AI-SDK toolset for microcharts — let a model find the right word-sized chart, get exactly how to wire it, and render it to a finished SVG with real alt text.
microcharts is built so a model can write charts. This is how a model calls them: not "emit some grammar and hope
the client renders it," but a tool call that returns the real library's static SVG, honest by construction, with the
generated describeSeries accessible name attached.
- Runs on your machine. stdio server, distributed on npm. Nothing is hosted; no account, no key, no data leaves.
- Works anywhere. The render tool returns self-contained SVG, so a chat surface that can't run React can still show a real chart.
- Zero setup.
npx @microcharts/mcp— the package brings its own copy of@microcharts/react.
Install
Add it to any MCP client. For Claude Desktop (claude_desktop_config.json) or Cursor (.cursor/mcp.json):
{
"mcpServers": {
"microcharts": {
"command": "npx",
"args": ["-y", "@microcharts/mcp"]
}
}
}
That's it — the client spawns the server over stdio on demand.
Tools
find_microchart(question, dataShape?)
Rank the catalog against a plain-language question. Start here when you know the question, not the chart.
find_microchart({ question: "is it trending?" })
→ [{ slug: "sparkline", name: "Sparkline", why: "inline trend", dataShape: "number[]" }, …]
get_microchart(slug)
Everything needed to wire one chart: import paths, its props plus the shared props, data shape, best/avoid guidance, a
copy-runnable example, and sample — that same example as JSON props, ready to pass straight to render_microchart.
get_microchart({ slug: "bullet" })
→ {
staticImport: "@microcharts/react/bullet",
props: [...],
example: { code: "<Bullet value={72} target={80} bands={[50, 90]} title=\"Quota\" />" },
sample: { value: 72, target: 80, bands: [50, 90], title: "Quota" },
…
}
Every chart takes its own data shape — number[] for a sparkline, { label, value }[] for a dot plot,
{ open, high, low, close }[] for OHLC — so sample is the shortest path from "which chart" to "a render that works".
render_microchart(type, data?, props?, format?)
Render to a finished, self-contained SVG plus its generated alt text. Pass the series as data; put other props
(value, target, curve, color, width) in props.
render_microchart({ type: "sparkline", data: [132, 148, 141, 165, 159, 182] })
→ {
svg: "<svg …><style>…</style>…</svg>",
summary: "Trending up 38%. Range 132 to 182. Last value 182.",
mimeType: "image/svg+xml", width: 80, height: 20
}
format is "svg" (default, styles embedded — drops into any surface) or "bare" (no CSS, for pages that already load
@microcharts/react/styles.css).
Missing or wrong-shaped data is rejected by name rather than passed through to React, so a caller gets
"dot-plot" needs \data`. Data shape: { label, value }[]. Call get_microchart("dot-plot") for a ready-to-render
sample.` instead of a stack trace. Oversized payloads are refused too — a microchart is a word-sized mark.
It also exposes two resources: microcharts://catalog (every chart + metadata) and microcharts://agent-setup (the
prompt for wiring microcharts into a codebase).
Use it from the Vercel AI SDK
The same three capabilities ship as AI-SDK tools (on the /ai-sdk subpath, so the core never pulls in ai):
import { microchartsTools } from "@microcharts/mcp/ai-sdk";
import { streamText } from "ai";
const result = streamText({
model,
tools: microchartsTools,
prompt: "Summarise this quarter and show the revenue trend as a chart.",
});
The functions are exported directly too — findChart, getChart, renderChart — if you want them without the tool
wrapper.
Versioning
This server bundles the chart catalog and renders with @microcharts/react (installed as a normal dependency). Its
version tracks its own changes; a compatible library is pulled in automatically. The library version a given snapshot
was cut from is on the microcharts://catalog resource.
Links
- Docs: https://microcharts.dev/docs/mcp
- Library:
@microcharts/react - Source: https://github.com/ganapativs/microcharts/tree/main/packages/mcp
MIT
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.