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Dataset Explorer

by khanarmaghanrasheed-18version 0.2.2i★ –iRuns on your computer
Registry nameio.github.khanarmaghanrasheed-18/dataset-explorer

Install

Overview

Description from the maker (original English)

Local MCP analysis for CSV, TSV, Excel, JSON and Parquet: summaries, outliers and relationships.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Databases & datasets

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • The registry lists no key to fill in.

You need

  • PyPIPython’s uv installed (for the uvx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "dataset-explorer": {
      "command": "uvx",
      "args": [
        "dataset-explorer-mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add dataset-explorer -- uvx dataset-explorer-mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "dataset-explorer": {
      "command": "uvx",
      "args": [
        "dataset-explorer-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "dataset-explorer": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "dataset-explorer-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account khanarmaghanrasheed-18, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Databases & datasetsi

  • More stars than 0% of the other 954 servers in this category
  • Published to the registry more recently than 67% of the other 954 servers in this category
  • Basic checks equal to or better than 100% of the other 954 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Dataset Explorer safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.2.2) reached the registry 28 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Supabase, MongoDB MCP Server, Elasticsearch MCP Server

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Supabase

by supabase

Supabase’s official server lets AI create tables, query data and review project settings in your Supabase database.

Computer or cloud★ 2,993Apache-2.0Updated today
Install

MongoDB MCP Server

by mongodb-js

MongoDB Model Context Protocol Server

Runs on your computer★ 1,157Apache-2.0Updated 2 days ago
Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Dataset Explorer — MCP server for local data analysis

Download on PyPI · Official MCP Registry · Glama directory

Ask your AI assistant questions about a dataset on your computer. Dataset Explorer does the calculations so your assistant can explain what is in the file, what needs attention, and which columns are related.

For example: "Explore my sales file. Are any values missing? Are there repeated rows? Which columns are related to revenue?"

Your original file stays unchanged. There is no website to host and no Gemini API key to set up.

What can it help with?

  • Summarize your data, including row counts, columns, averages, and medians.
  • Find missing values, repeated rows, and unusual numbers.
  • Look closely at a column you care about.
  • Compare columns and explore their relationships.

Supported files: CSV, TSV, Excel (`.xlsx` and `.xls`), JSON, and Parquet. For Excel, the first worksheet is used. JSON files should contain table-like data.

Get started

1. Install it

You need Python 3.10 or newer. Run this in a terminal:

python -m pip install dataset-explorer-mcp

This downloads Dataset Explorer and the libraries it needs. You can also find it on PyPI.

2. Connect your AI assistant

Use Claude Desktop, Cursor, or VS Code with Copilot. These apps can connect to local tools through MCP. Choose your app below for the settings to add.

Claude Desktop or Cursor

For Claude Desktop, open Settings > Developer > Edit Config. For Cursor, use its MCP settings or the .cursor/mcp.json file in your project. Add this entry to your configuration, keeping any servers you already have:

{

"mcpServers": { "dataset-explorer": { "command": "python", "args": ["-m", "mcp_server"] } } }

VS Code with Copilot

Create or open .vscode/mcp.json in your project and add this server:

{

"servers": { "dataset-explorer": { "type": "stdio", "command": "python", "args": ["-m", "mcp_server"] } } }

Restart your app or reload its MCP settings. It will start Dataset Explorer for you. If it cannot find Python, see the quick fixes below.

3. Ask about a file

Give your assistant the full path to the file and ask your question:

Use Dataset Explorer to summarize C:/Users/YourName/Downloads/sales.csv.

Check for missing values and repeated rows, then explain the results.

On macOS or Linux, use a path such as /Users/yourname/data/sales.csv or /home/yourname/data/sales.csv.

The file must be on the computer where the server runs. Your assistant may send the calculated results to its AI provider according to that app's settings.

How does it find relationships?

It calculates established statistics directly from your dataset:

  • Pearson correlation: how two numeric columns move together.
  • Eta squared: how numeric values differ between groups.
  • Cramer's V: how two category columns are related.

It chooses the method to suit the columns. These results help you spot patterns; they do not prove that one thing causes another. Results depend on the data you provide.

Quick fixes

  • Python or the package cannot be found: run python -c "import sys; print(sys.executable)" in the terminal where you installed it. Use the printed path in place of python in your app's configuration. On Windows, use forward slashes in that path.
  • File not found: give the full file path and check that your assistant has permission to read it.
  • No tools appear: reload the MCP settings and check your app's server logs.
  • The terminal seems idle: that is normal. This server waits for your assistant to connect; it does not open a chat window of its own.

Large files need enough memory because the server loads the dataset for each request.

For developers: tools and running from source

The server uses local stdio. Start it with dataset-explorer-mcp or python -m mcp_server. Logs go to stderr; stdout carries MCP messages.

Its ten tools are get_dataset_overview, dataset_shape, dataset_statistical_summary, inspect_Column, analyze_target, duplicate_finder, analyze_missing_values, find_correlations, detect_outliers, and screen_target_relationships. Every tool accepts a local file path. A guide is available at dataset://guide, along with an explore_dataset prompt.

After cloning this repository:

python -m pip install -e ".[dev]"

python -m pytest -q python mcp_server.py

Build a package with python -m build.

MIT licensed. See LICENSE.

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