MCP Connector

Access and Analyze Hugging Face Datasets

An MCP server for the Hugging Face Dataset Viewer API - browse, search, filter, and analyze Hub datasets.

Works with huggingface

90
Spark score
out of 100
Updated Apr 2025
Version 1.0.0
Models
universal

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Why it matters

Effortlessly interact with Hugging Face datasets. View, analyze, search, and filter datasets hosted on the Hugging Face Hub using a dedicated MCP server.

Outcomes

What it gets done

01

Validate dataset existence and accessibility

02

Retrieve and paginate dataset content

03

Search and filter dataset rows using SQL-like conditions

04

Download datasets in Parquet format

Install

Add it to your toolbox

Run in your project directory:

curl -fsSL https://spark.entire.vc/get/vb-dataset-viewer | bash

Capabilities

Tools your agent gets

validate

Checks whether a dataset exists and is accessible

get_info

Retrieves detailed information about a dataset

get_rows

Retrieves dataset content with pagination

get_first_rows

Retrieves the first rows from a dataset split

get_statistics

Retrieves statistics for a dataset split

search_dataset

Searches for text in a dataset

filter

Filters rows using SQL-like conditions

get_parquet

Downloads the full dataset in Parquet format

Overview

Dataset Viewer MCP Server

This MCP server exposes eight tools for the Hugging Face Dataset Viewer API: validating, inspecting, paginating, searching, SQL-filtering, and downloading datasets as Parquet. Use it when an AI assistant needs to explore, validate, or query Hugging Face Hub datasets. Private datasets require a Hugging Face API token.

What it does

An MCP server for the Hugging Face Dataset Viewer API, letting an AI assistant browse and analyze datasets hosted on the Hugging Face Hub. It exposes eight tools: validate checks whether a dataset exists and is accessible; get_info retrieves detailed information about a dataset; get_rows returns paginated contents of a dataset given a config and split, with an optional page number; get_first_rows returns just the first rows of a split without pagination; get_statistics returns statistics about a dataset split; search_dataset searches for a text query within a split; filter applies SQL-like conditions to a split via a WHERE clause (e.g. "score > 0.5") with optional ORDER BY and pagination; and get_parquet downloads an entire dataset in Parquet format. It also exposes datasets as resources under a dataset:// URI scheme, supporting dataset configurations and splits, pagination, authentication for private datasets, search and filtering, and dataset statistics as first-class MCP resources rather than only tool calls.

When to use - and when NOT to

Use it when an AI assistant needs to check whether a Hugging Face dataset exists, inspect its structure and statistics, page through or search its contents, run SQL-like filters against a split, or download it wholesale as Parquet - for exploring or validating datasets before using them in a pipeline. Private datasets require a Hugging Face API token; public datasets need no authentication. It is not a general Hugging Face Hub browser - it is scoped specifically to the Dataset Viewer API's dataset-inspection capabilities.

Capabilities

Eight tools spanning existence/accessibility checking (validate), metadata retrieval (get_info, get_statistics), paginated and first-N-row content access (get_rows, get_first_rows), full-text search (search_dataset), SQL-like filtering with WHERE/ORDER BY (filter), and bulk Parquet download (get_parquet) - all parameterized by dataset identifier, config name, and split name, with an optional auth_token for private datasets on every tool.

How to install

git clone https://github.com/privetin/dataset-viewer.git
cd dataset-viewer
uv venv
source .venv/bin/activate
uv add -e .

Requires Python 3.12 or higher and uv. Configure the optional HUGGINGFACE_TOKEN environment variable for private-dataset access. A Claude Desktop config points uv at the cloned repository's directory and runs the dataset-viewer command:

{
  "mcpServers": {
    "dataset-viewer": {
      "command": "uv",
      "args": ["--directory", "parent_to_repo/dataset-viewer", "run", "dataset-viewer"]
    }
  }
}

Who it's for

Data scientists and ML engineers who want to explore, validate, and query Hugging Face Hub datasets conversationally - checking a dataset's structure, searching or filtering its contents, or pulling statistics and Parquet exports - before wiring it into a training or evaluation pipeline.

Source README

Dataset Viewer MCP Server

An MCP server for interacting with the Hugging Face Dataset Viewer API, providing capabilities to browse and analyze datasets hosted on the Hugging Face Hub.

Features

Resources

  • Uses dataset:// URI scheme for accessing Hugging Face datasets
  • Supports dataset configurations and splits
  • Provides paginated access to dataset contents
  • Handles authentication for private datasets
  • Supports searching and filtering dataset contents
  • Provides dataset statistics and analysis

Tools

The server provides the following tools:

  1. validate

    • Check if a dataset exists and is accessible
    • Parameters:
      • dataset: Dataset identifier (e.g. 'stanfordnlp/imdb')
      • auth_token (optional): For private datasets
  2. get_info

    • Get detailed information about a dataset
    • Parameters:
      • dataset: Dataset identifier
      • auth_token (optional): For private datasets
  3. get_rows

    • Get paginated contents of a dataset
    • Parameters:
      • dataset: Dataset identifier
      • config: Configuration name
      • split: Split name
      • page (optional): Page number (0-based)
      • auth_token (optional): For private datasets
  4. get_first_rows

    • Get first rows from a dataset split
    • Parameters:
      • dataset: Dataset identifier
      • config: Configuration name
      • split: Split name
      • auth_token (optional): For private datasets
  5. get_statistics

    • Get statistics about a dataset split
    • Parameters:
      • dataset: Dataset identifier
      • config: Configuration name
      • split: Split name
      • auth_token (optional): For private datasets
  6. search_dataset

    • Search for text within a dataset
    • Parameters:
      • dataset: Dataset identifier
      • config: Configuration name
      • split: Split name
      • query: Text to search for
      • auth_token (optional): For private datasets
  7. filter

    • Filter rows using SQL-like conditions
    • Parameters:
      • dataset: Dataset identifier
      • config: Configuration name
      • split: Split name
      • where: SQL WHERE clause (e.g. "score > 0.5")
      • orderby (optional): SQL ORDER BY clause
      • page (optional): Page number (0-based)
      • auth_token (optional): For private datasets
  8. get_parquet

    • Download entire dataset in Parquet format
    • Parameters:
      • dataset: Dataset identifier
      • auth_token (optional): For private datasets

Installation

Prerequisites

  • Python 3.12 or higher
  • uv - Fast Python package installer and resolver

Setup

  1. Clone the repository:
git clone https://github.com/privetin/dataset-viewer.git
cd dataset-viewer
  1. Create a virtual environment and install:
# Create virtual environment
uv venv

# Activate virtual environment
# On Unix:
source .venv/bin/activate
# On Windows:
.venv\Scripts\activate

# Install in development mode
uv add -e .

Configuration

Environment Variables

  • HUGGINGFACE_TOKEN: Your Hugging Face API token for accessing private datasets

Claude Desktop Integration

Add the following to your Claude Desktop config file:

On Windows: %APPDATA%\Claude\claude_desktop_config.json

On MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "dataset-viewer": {
      "command": "uv",
      "args": [
        "--directory",
        "parent_to_repo/dataset-viewer",
        "run",
        "dataset-viewer"
      ]
    }
  }
}

FAQ

Common questions

Discussion

Questions & comments · 0

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