validateLABSCO SUMMARY
Decide whether a dataset is the right one before you download it.
The Dataset Viewer API answers what is in a split, what the columns look like and whether the text you need is actually in there - so the decision gets made from a handful of calls instead of a clone.
What it is
A server over the Hugging Face Dataset Viewer API. It reads datasets on the Hub without cloning them: what configurations and splits exist, paginated rows, per-split statistics, and search and SQL-style filtering over the contents.
What you get
- A dataset's detail - its configurations and its splits - before anything is downloaded
- The first rows of a split, and any page of it after that
- Statistics for a split, so the distribution is visible without loading the data
- Text searched across a split, and rows filtered with a SQL WHERE clause and an ORDER BY
- The whole dataset pulled down in Parquet, once you have decided it is the right one
- Private datasets reachable by passing a token on the call
Requirements
Python 3.12 or newer. Public datasets need no credential; a private one needs a Hugging Face token, passed per call as auth_token rather than configured once. Datasets are addressed by their Hub identifier - stanfordnlp/imdb, for example.
Setup effort
Build from source — clone the repository, then follow the steps on this page and add the configuration for your client
USE CASES
01
Sizing up a dataset you just found
Read its configurations and splits, then take the first rows to see the columns for real.
02
Checking a split contains what you need
Search it for the term that matters to you before committing to the dataset.
03
Understanding the shape before training on it
Pull the statistics for the split, so class balance and field distributions are visible up front.
04
Reading through a dataset page by page
Page the rows with the config and split named, rather than loading the whole thing to look at a hundred examples.
05
Taking it locally once you have decided
Download the dataset in Parquet, after the earlier calls have justified the bandwidth.
8 tools
Listed in project docs. Your client may show a different list.
get_infoget_rowsget_first_rowsget_statisticssearch_datasetfilterget_parquetBEFORE YOU INSTALL
What to know first
Tools you get
8 tools named in the project's documentation.
Requires
- uv on PATH
- Python 3.12 or later
Where it runs
The documented client runs the server on your machine.
INSTALLATION
Connect your client
Clone the repository, then follow the steps on this page and add the configuration for your client. This one is built from source rather than installed with a single command.
Server setup
Setup
TERMINAL
git clone https://github.com/privetin/dataset-viewer.git
cd dataset-viewer
uv venv
source .venv/bin/activate
uv add -e .uv
TERMINAL
uv --directory <YOUR_PATH_TO_DATASET_VIEWER> run dataset-viewerMCP Clients
Claude Desktop
CLAUDE DESKTOP · JSON
{
"mcpServers": {
"dataset-viewer": {
"command": "uv",
"args": [
"--directory",
"<YOUR_PATH_TO_DATASET_VIEWER>",
"run",
"dataset-viewer"
]
}
}
}<YOUR_PATH_TO_DATASET_VIEWER> is the full path to the dataset-viewer folder the git clone step created.
