data-explore
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 |
|---|---|
| analyze_datasetB | Analyze dataset and return actual results. Args: dataset_path: Path to the dataset file (CSV format) analysis_type: Type of analysis - "summary", "correlation", "distribution", "missing_values" columns: Specific columns to analyze (if None, analyzes all columns) |
| clean_dataA | Perform data cleaning operations and return results. Args: dataset_path: Path to the dataset file (CSV format) operations: List of cleaning operations - "remove_nulls", "fill_nulls", "remove_duplicates", "standardize_columns", "convert_types" output_path: Path to save cleaned dataset (optional) |
| statistical_summaryA | Perform statistical analysis and return results. Args:
dataset_path: Path to the dataset file (CSV format) |
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 3 tools
The tools clean_data and statistical_summary are fairly distinct, but analyze_dataset overlaps with statistical_summary in correlation and summary analyses. An agent might confuse which tool to use for correlation analysis between the two. Some descriptions help clarify the intent.
Most tools follow a verb_noun pattern (analyze_dataset, clean_data), but statistical_summary deviates as an adjective_noun phrase. This is a minor inconsistency that could confuse prediction of tool names.
Three tools is on the low side but fits the focused domain. Each tool covers a broad category of operations, which keeps the surface manageable. The count seems appropriate for a simple data exploration server.
Core data exploration tasks are covered: analyzing, cleaning, and statistical testing. Missing capabilities like data preview or visualization are minor and can be worked around. The overlap between analyze_dataset and statistical_summary leaves some statistical tests only in one tool, but the surface is generally complete.