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Csvglow

by Ratnaditya-J

Creates offline, self-contained HTML dashboards from CSV and Excel files with charts, analysis, and sortable tables.

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Install Csvglow

Pick your client, then add it in one click or paste the config.

STDIONode.js
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "ratnaditya-j-csvglow": {
      "command": "npx",
      "args": [
        "-y",
        "csvglow"
      ]
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

We couldn’t confirm it starts correctly

We ran the install command but it didn't respond within our test window — this can mean a slow first-time install rather than a real problem.

npx -y csvglow

No response to initialize.

This is an experimental automated check and can have false negatives — missing environment variables, a slow cold install, etc. It doesn’t necessarily mean something’s wrong. Last checked 1mo ago.

Overview

Ratnaditya-J/csvglow MCP server generates interactive HTML dashboards from CSV, TSV, XLS, and XLSX files. It detects column types and produces charts, cross-column findings, correlations, and a sortable data table in one self-contained file. Use it when an MCP-compatible assistant needs to turn a local data file into a browser-viewable dashboard without a server or CDN. Cursor and Windsurf can run it through an npx MCP configuration.

Use cases

  • Generate an offline dashboard from a CSV file
  • Analyze Excel data with automatically selected charts
  • Inspect correlations and outliers across numeric columns
  • Share a self-contained HTML report with sortable source data

Key features

  • CSV, TSV, XLS, and XLSX input
  • Automatic column-type detection
  • Interactive ECharts visualizations
  • Correlation and cross-column analysis
  • Sortable and filterable data table
  • Offline self-contained HTML output

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked 2d ago

Recent health check succeeded.

18/19 checks healthy over the last 79d

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 10d ago via OSV.dev · csvglow (npm)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars14Stargazers on the source repository.
npm downloads40Package downloads in the last 30 days.
Last commit7mo agoMost recent push to the default branch.
Install checkInconclusiveDidn't respond in our test window — often a slow first install.
Directory activity6 viewsConfig copies, upvotes, and views on AllMCPs.

How Csvglow works

What Ratnaditya-J/csvglow MCP server does

Ratnaditya-J/csvglow MCP server turns a local tabular file into an interactive HTML dashboard. The generated output is a single file that includes its visualizations and interface, so it can be opened in a browser and used offline without a separate application server or CDN.

The package accepts CSV and TSV files, with delimiter detection for those formats, as well as XLS and XLSX workbooks. XLSX processing currently uses the first sheet. The dashboard includes a sortable and filterable table showing the first 1,000 rows, along with visual analysis based on the detected column types.

How it works

The MCP mode runs locally and lets an MCP-compatible assistant request a dashboard from a file path. The underlying command can also be used directly from a terminal. When processing a file, csvglow identifies numeric, categorical, date/time, and identifier columns, then chooses relevant views automatically.

Numeric fields can receive histograms with descriptive statistics, including mean, median, standard deviation, quartiles, and outlier counts. Categorical fields can be represented with bar charts. Date columns can produce time-series charts with area fills. The generator also creates categorical-versus-numeric cross-analysis, a numeric correlation heatmap, and scatter plots for pairs whose absolute correlation exceeds 0.7.

The output includes multi-column findings that compare metrics and may point out contradictions, efficiency gaps, or high- and low-performing records. Large inputs may use smart sampling, including files with 100,000 or more rows. Each chart has a control for copying it to the clipboard.

Setup and configuration

Install the package from PyPI with pip install csvglow, or run it through npx without a separate package installation. The direct command accepts an input file, an optional output path, and a flag to suppress automatic browser opening. For example, csvglow data.csv -o dashboard.html writes to a chosen location, while csvglow data.csv --no-open avoids opening the result automatically.

To expose Ratnaditya-J/csvglow MCP server through npx, configure the client with command npx and arguments -y, csvglow, and --mcp. An existing pip installation can instead use the csvglow executable with the --mcp argument.

Tools and capabilities

The documented MCP use case is dashboard generation from a file path. Resulting dashboards can contain:

  • Automatically selected histograms, bar charts, time-series views, cross-analysis charts, heatmaps, and scatter plots
  • Narrative findings based on relationships across multiple columns
  • A sortable and filterable table limited to the first 1,000 rows
  • Clipboard copying for individual charts
  • A self-contained HTML result suitable for offline viewing

Limitations and notes

Multi-sheet Excel processing is not supported yet; only the first XLSX sheet is used. Multi-file inputs, join keys, light themes, custom color palettes, and PDF export are listed as roadmap items rather than current capabilities. The repository describes the software as MIT-licensed.

Read the full README View source on GitHub

Reviews

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Frequently Asked Questions about Csvglow

Install it with `pip install csvglow`, or configure an MCP client to run `npx -y csvglow --mcp`.

It documents MCP configuration for Cursor and Windsurf.

It supports CSV and TSV files, plus XLS and XLSX workbooks. XLSX processing currently uses the first sheet.

Yes. The generated dashboard is a self-contained HTML file that works offline without a server or CDN.

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