mcp-server-data-exploration
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 |
|---|---|
| load_csvB | Load CSV File Tool Purpose: Load a local CSV file into a DataFrame. Usage Notes: • If a df_name is not provided, the tool will automatically assign names sequentially as df_1, df_2, and so on. |
| run_scriptB | Python Script Execution Tool Purpose: Execute Python scripts for specific data analytics tasks. Allowed Actions 1. Print Results: Output will be displayed as the script’s stdout. 2. [Optional] Save DataFrames: Store DataFrames in memory for future use by specifying a save_to_memory name. Prohibited Actions 1. Overwriting Original DataFrames: Do not modify existing DataFrames to preserve their integrity for future tasks. 2. Creating Charts: Chart generation is not permitted. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| explore-data | A prompt to explore a csv dataset as a data scientist |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| Data Exploration Notes | Notes generated by the data exploration server |
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: load_csv is for loading CSV files into DataFrames, while run_script is for executing Python scripts for data analytics tasks. There is no overlap in functionality, and an agent would easily distinguish between them.
Both tools use snake_case naming, which is consistent, but they follow different patterns: load_csv uses a verb_noun format, while run_script uses verb_noun as well but with a more generic noun. This minor deviation keeps it mostly consistent but not perfectly aligned.
With only two tools, the server feels severely under-scoped for data exploration. Key operations like data transformation, filtering, aggregation, or visualization are missing, making it incomplete for typical data analysis workflows.
The tool set is highly incomplete for data exploration. It covers only loading data and running scripts, with no tools for common tasks like data cleaning, analysis, or exporting results. This will likely cause agent failures when trying to perform comprehensive data exploration.