Zaturn
<h1>
<img src="https://github.com/kdqed/zaturn/raw/main/zaturn/studio/static/logo.png" width="24" height="24">
<span>Zaturn: Your Co-Pilot For Data Analytics & Business Insights</span>
</h1>
## Just Chat With Your Data! No SQL, No Python.
Zaturn provides tools that enable AI models to run SQL, so you don't have to. It can be used as an MCP or as a web interface similar to Jupyter Notebook.
## Zaturn in Action
https://github.com/user-attachments/assets/d42dc433-e5ec-4b3e-bef0-5cfc097396ab
## Features:
### Multiple Data Sources
Zaturn can currently connect to the following data sources:
- SQL Databases: PostgreSQL, SQLite, DuckDB, MySQL, ClickHouse, SQL Server, BigQuery
- Files: CSV, Parquet
[Request data source by raising an issue](https://github.com/kdqed/zaturn/issues/new)
### Visualizations
In addition to providing tabular and textual summaries, Zaturn can also generate the following image visualizations
- Scatter and Line Plots
- Histograms
- Strip and Box Plots
- Bar Plots
- Density Heatmap (aka. 2D Histograms)
- Polar Scatter and Line Plots
[Request visualizations by raising an issue](https://github.com/kdqed/zaturn/issues/new)
## Installation & Setup
See [https://zaturn.pro/docs/install](https://zaturn.pro/docs/install)
## Roadmap
- Support for more data source types & more data visualizations as per community requests
- Dashboard building: Pin queries and visuals to re-run without LLM calls.
- Multi-user capabilities
- Predictive Analytics and ML features
## Help And Feedback
[Raise an issue](https://github.com/kdqed/zaturn/issues)
## Support The Project
If you find Zaturn useful, please support this project by:
- Starring the project
- Sponsoring the development
- Spreading the word
Your support will enable me to dedicate more of my time to Zaturn.
## Example Dataset Credits
The [pokemon dataset compiled by Sarah Taha and PokéAPI](https://www.kaggle.com/datasets/sarahtaha/1025-pokemon) has been included under the [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license for demonstration purposes.
## Featured on glama.ai
<a href="https://glama.ai/mcp/servers/@kdqed/zaturn">
<img width="380" height="200" src="https://glama.ai/mcp/servers/@kdqed/zaturn/badge" alt="Zaturn MCP server" />
</a>
## Star History
[](https://www.star-history.com/#kdqed/zaturn&Date)
TDQS
Scored across 12 tools
Every tool has a clearly distinct purpose: 10 visualization tools each create different plot types (bar, box, scatter, etc.), 'run_query' returns raw data, 'list_data_sources' enumerates sources, and 'describe_table' provides schema information. There is no functional overlap between tools - an agent can easily select the right tool for each task.
All tools follow a consistent snake_case naming convention with clear verb_noun patterns: visualization tools use plot_type names (bar_plot, scatter_plot), while utility tools use action_object patterns (run_query, list_data_sources, describe_table). The naming is perfectly predictable and follows a single convention throughout.
12 tools is well-scoped for a data visualization and query server. The count includes comprehensive visualization coverage (10 plot types), core data operations (run_query), and metadata utilities (list_data_sources, describe_table). Each tool earns its place without redundancy or bloat.
The toolset provides excellent coverage for data exploration and visualization: query execution, schema inspection, source enumeration, and multiple plot types. Minor gaps include lack of data modification tools (update/delete) and advanced analytics functions, but these may be outside the server's intended scope as a visualization-focused tool.