mcpField
C
Csvglow

Csvglow

Local
github.com/Ratnaditya-JData & Analytics14Updated 32w ago

About Csvglow

Csvglow turns CSV, TSV, and Excel files into interactive, self-contained HTML dashboards for people exploring tabular data. It detects column types and builds charts, findings, and a filterable table.

Key features

  1. 01Auto-detect column types and create charts
  2. 02Multi-column findings and cross-analysis
  3. 03Correlation heatmaps and scatter plots
  4. 04Sortable and filterable data table
  5. 05Copy charts to clipboard
  6. 06Create offline, self-contained HTML

Use cases

  1. 01Quickly inspect CSV or Excel data
  2. 02Compare categories with numeric metrics
  3. 03Explore correlations between numeric columns
  4. 04Copy charts into presentations

MCP support

Csvglow can run as an MCP tool. Once configured, an MCP client can request a dashboard by providing a file path.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"csvglow": {
"command": "npx",
"args": [
"-y",
"csvglow"
]
}
}
}
Run commandnpx -y csvglow

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npmcsvglow

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

csvglow

csvglow MCP server

Generate beautiful, interactive HTML dashboards from CSV/Excel files. One command, zero config.

csvglow sales.csv

Opens a self-contained HTML dashboard in your browser with auto-detected charts, smart multi-column insights, correlations, and a sortable data table. Dark gradient theme. Copy any chart to your clipboard.

Install

pip install csvglow

Or via npx (no install needed):

npx csvglow data.csv

Usage

csvglow data.csv                    # CSV to dashboard, opens in browser
csvglow report.xlsx                 # Excel works too
csvglow data.csv -o dashboard.html  # Custom output path
csvglow data.csv --no-open          # Don't auto-open browser

What it generates

  • Smart findings — multi-column narrative analysis that cross-references metrics to surface contradictions, efficiency gaps, and top/underperformers
  • Histograms for every numeric column with mean, median, std, quartiles, and outlier counts
  • Bar charts for categorical columns
  • Cross analysis — automatic categorical x numeric crosstabs with overall mean lines
  • Time series line charts with area fill for date columns
  • Correlation heatmap between numeric columns
  • Scatter plots for highly correlated pairs (|r| > 0.7)
  • Sortable, filterable data table (first 1000 rows)
  • Copy button on each chart for pasting into slides

Output is a single self-contained HTML file. No server, no CDN, works offline.

MCP Server

csvglow works as an MCP tool in any MCP-compatible client. Once configured, ask your AI assistant to generate a dashboard from a file path.

Pick your client and add csvglow to its MCP config file:

ClientConfig file location
Cursor.cursor/mcp.json in your project root
Windsurf~/.windsurf/mcp.json

Add this to the config:

{
  "mcpServers": {
    "csvglow": {
      "command": "npx",
      "args": ["-y", "csvglow", "--mcp"]
    }
  }
}

Uses npx so there's nothing extra to install.

If you already have csvglow installed via pip, use "command": "csvglow" with "args": ["--mcp"] instead.

OpenClaw Skill

csvglow is available as an OpenClaw skill. Any OpenClaw-compatible client can discover and use it automatically — no manual config needed.

Supported formats

  • .csv / .tsv (auto-detected delimiter)
  • .xls
  • .xlsx (first sheet only — multi-sheet support coming soon)

Changelog

0.1.0
  • Initial release
  • Auto-detection of column types (numeric, categorical, datetime, identifier)
  • Smart findings: contradiction detection, efficiency analysis, top/underperformer identification across multiple columns
  • Histograms with stats sidebar, bar charts, cross-analysis crosstabs, time series, correlation heatmap, scatter plots
  • Sortable/filterable data table
  • Copy-to-clipboard for all charts
  • MCP server mode (csvglow --mcp)
  • OpenClaw skill support
  • Smart sampling for large files (100k+ rows)

Roadmap

  • Multi-sheet Excel support
  • Multi-file support with join keys
  • Light theme
  • Custom color palettes
  • PDF export

License

MIT

Comments

Markdown supported

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.