About Ratnaditya-J/csvglow
Ratnaditya-J/csvglow is an MCP server in the Files category: contained HTML dashboards from CSV/Excel files with interactive ECharts visualizations, dark gradient theme, and sortable data tables.
Install
git clone https://github.com/Ratnaditya-J/csvglowThis server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.
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README
csvglow
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:
| Client | Config 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
README copy captured when this server was indexed; it may lag the repository. The original is authoritative.