Dataset Explorer MCP Server
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
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| get_dataset_overviewB | Returns dataset features, missing-value counts, categorical columns, numerical columns, and data types. |
| dataset_shapeB | Returns the number of rows and columns in a CSV dataset. |
| dataset_statistical_summaryA | Returns the mean and median of every numerical feature. |
| inspect_ColumnC | Gives a detailed summary of a specific column/feature. |
| analyze_targetC | Gives a detailed summary of the target column. |
| duplicate_finderC | Reports duplicate rows in the dataset. |
| analyze_missing_valuesC | Reports columns with missing values in the dataset. |
| find_correlationsA | Finds strongly correlated numerical feature pairs above a specified absolute threshold. |
| detect_outliersC | Detects Outliers in the dataset. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| explore_dataset |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| dataset_guide |
TDQS
Scored across 9 tools
Some overlap exists between get_dataset_overview and analyze_missing_values, as both report missing-value counts. Additionally, inspect_Column and analyze_target are similar in nature, though they target different columns. Most other tools are clearly distinct.
Naming is inconsistent: some tools use verb_noun format (get_dataset_overview, analyze_target, find_correlations) while others use noun phrases (dataset_shape, duplicate_finder). The tool inspect_Column mixes snake_case with camelCase.
Nine tools is well-scoped for a dataset exploration server, covering essential analysis tasks without redundancy.
The tool set covers a broad range of dataset exploration needs including shape, stats, missing values, duplicates, correlations, and outliers. A data preview or column value counts would be a minor addition, but no critical gaps are apparent.