Csvglow
Generate self-contained HTML dashboards from CSV/Excel files with interactive ECharts visualizations and sortable data tables.
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
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
Frequently asked questions
What is Csvglow?
Generate self-contained HTML dashboards from CSV/Excel files with interactive ECharts visualizations and sortable data tables.
How do I install Csvglow?
Follow the installation steps in its documentation, shown on this page and in its repository.
Is Csvglow open source?
Yes. Its code is on GitHub under the MIT license.
Who maintains Csvglow?
It is published by Ratnaditya-J. 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.