官方说明7 个来源MCP 服务8 天前
Ratnaditya-J/csvglow
Generate self-contained HTML dashboards from
在 GitHub 查看 ↗直链 · 无中转页
配置模板（请按你的客户端调整）
claude mcp add csvglow -- npx -y csvglow
这是根据仓库名生成的可粘贴骨架，命令行参数与包名请以该项目 README 为准。
- ★ 数取值
- 14 ★ · Ratnaditya-J/csvglow · 取自 GitHub 搜索 API · （2026-10-16 20:00:20 核对）
- 来源明细
- 收录自 punkpeye/awesome-mcp-servers 榜单（我们从中收集的条目之一） · 条目类型：MCP 服务
- 收录与活跃
- 收录于 2026-10-09 · 最近见到 2026-10-16 · 共见到 32 次 · 被 7 个来源列出
- 评分构成
- 综合分 703 · 14 ★ · 7 个来源 · mcp
常见问题（答案都来自我们的记录）
怎么安装？
我们【没有】从该仓库取到安装命令（我们的 install 覆盖率只有约 4%）。请看它的 README 或 releases 页。
它适合谁 / 用来做什么？
我们把它归在这些标签下: MCP 服务。标签来自条目自身，不是我们强加的分类。
谁维护它、还在更新吗？
仓库
Ratnaditya-J/csvglow；我们收录于 2026-10-09；最近见到 2026-10-16。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。第三方质量标记
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
- 来源
- awesome · https://github.com/Ratnaditya-J/csvglow
- 按领域浏览
- MCP 服务
- Traction
- 14 ★
- 收录于
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