Zaturn
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| list_data_sourcesA | List all available data sources. Returns a list of unique source_ids to be used for other queries. Source type is included in the source_id string. While drafting SQL queries use appropriate syntax as per source type. |
| describe_tableB | Lists columns and their types in the specified table of specified data source. |
| run_queryA | Run query against specified source For both csv and parquet sources, use DuckDB SQL syntax Use 'CSV' as the table name for csv sources. Use 'PARQUET' as the table name for parquet sources. This will return a dataframe with the results. |
| scatter_plotC | Run query against specified source and make a scatter plot using result For both csv and parquet sources, use DuckDB SQL syntax Use 'CSV' as the table name in the SQL query for csv sources. Use 'PARQUET' as the table name in the SQL query for parquet sources. This will return an image of the plot |
| line_plotC | Run query against specified source and make a line plot using result For both csv and parquet sources, use DuckDB SQL syntax Use 'CSV' as the table name in the SQL query for csv sources. Use 'PARQUET' as the table name in the SQL query for parquet sources. This will return an image of the plot |
| histogramB | Run query against specified source and make a histogram using result For both csv and parquet sources, use DuckDB SQL syntax Use 'CSV' as the table name in the SQL query for csv sources. Use 'PARQUET' as the table name in the SQL query for parquet sources. This will return an image of the plot |
| strip_plotB | Run query against specified source and make a strip plot using result For both csv and parquet sources, use DuckDB SQL syntax Use 'CSV' as the table name in the SQL query for csv sources. Use 'PARQUET' as the table name in the SQL query for parquet sources. This will return an image of the plot |
| box_plotA | Run query against specified source and make a box plot using result For both csv and parquet sources, use DuckDB SQL syntax Use 'CSV' as the table name in the SQL query for csv sources. Use 'PARQUET' as the table name in the SQL query for parquet sources. This will return an image of the plot |
| bar_plotA | Run query against specified source and make a bar plot using result For both csv and parquet sources, use DuckDB SQL syntax Use 'CSV' as the table name in the SQL query for csv sources. Use 'PARQUET' as the table name in the SQL query for parquet sources. This will return an image of the plot |
| density_heatmapA | Run query against specified source and make a 2d-histogram aka. Density Heatmap using result For both csv and parquet sources, use DuckDB SQL syntax Use 'CSV' as the table name in the SQL query for csv sources. Use 'PARQUET' as the table name in the SQL query for parquet sources. This will return an image of the plot |
| polar_scatterB | Run query against specified source and make a polar scatter plot using result For both csv and parquet sources, use DuckDB SQL syntax Use 'CSV' as the table name in the SQL query for csv sources. Use 'PARQUET' as the table name in the SQL query for parquet sources. This will return an image of the plot |
| polar_lineA | Run query against specified source and make a polar line plot using result For both csv and parquet sources, use DuckDB SQL syntax Use 'CSV' as the table name in the SQL query for csv sources. Use 'PARQUET' as the table name in the SQL query for parquet sources. This will return an image of the plot |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
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.