MCP

Community Other

Word-sized charts (sparklines, bars, bullets and more) rendered to self-contained SVG with generated alt text, plus chart suggestions for a given question.

Install

The installation steps are in the documentation below. Documentation

Documentation

From the project's README on GitHub, refreshed weekly. Links open on GitHub.

Contents
  1. @microcharts/mcp
  2. Install
  3. Tools
  4. Use it from the Vercel AI SDK
  5. Versioning
  6. Links

@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.

MIT

Frequently asked questions

What is MCP?

Word-sized charts (sparklines, bars, bullets and more) rendered to self-contained SVG with generated alt text, plus chart suggestions for a given question.

How do I install MCP?

Follow the installation steps in its documentation, shown on this page and in its repository.

Is MCP open source?

Yes. Its code is on GitHub under the MIT license.

Who maintains MCP?

It is published by ganapativs. AI.tn lists it but does not maintain or endorse it.

From the community awesome-mcp-servers lists and the project's GitHub repository. Listing is not an endorsement: review a server's code and permissions before giving it access to your data.