Official description7 sourcesMCP servers8d ago
Ratnaditya-J/csvglow
Generate self-contained HTML dashboards from
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Config template (adjust for your client)
claude mcp add csvglow -- npx -y csvglow
A pasteable skeleton generated from the repository name. Check the project README for the exact package and arguments.
- Star count
- 14 ★ · Ratnaditya-J/csvglow · read from the GitHub search API · (checked 2026-10-16 20:00:20)
- Source
- collected from the punkpeye/awesome-mcp-servers list · type: MCP server
- Harvest history
- first seen 2026-10-09 · last seen 2026-10-16 · seen 32× · listed by 7 sources
- Score
- score 703 · 14 ★ · 7 sources · mcp
FAQ (answers come from our records)
How do I install it?
We did not find an install command for this repository (our install coverage is only about 4%). Check its README or releases page.
Who is it for?
We filed it under these tags: MCP 服务. The tags come from the entry itself, not a taxonomy we imposed.
Who maintains it?
repository
Ratnaditya-J/csvglow；first catalogued 2026-10-09；last seen 2026-10-16。There is no official certification or quality rating here — we record sources and dates only.Third-party quality signals
csvglow
以下摘录自该仓库的 README（片段） · 在 GitHub 读完整 README →
Generate beautiful, interactive HTML dashboards from CSV/Excel files. One command, zero config.
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
Or via npx (no install needed):
Usage
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:
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
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
Or via npx (no install needed):
Usage
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:
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
- Source
- awesome · https://github.com/Ratnaditya-J/csvglow
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