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Dataset Viewer MCP Server: Instant Insights & API Mastery - MCP Implementation

Dataset Viewer MCP Server: Instant Insights & API Mastery

Effortlessly explore, filter, and analyze Hugging Face datasets with Dataset Viewer MCP Server – your expert sidekick for instant insights and seamless API-powered data mastery." )

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About Dataset Viewer MCP Server

What is Dataset Viewer MCP Server: Instant Insights & API Mastery?

This server acts as a streamlined interface for interacting with the Hugging Face Dataset Viewer API, enabling developers to rapidly access, analyze, and manipulate datasets hosted on the Hugging Face Hub. It provides a unified access layer through the dataset:// protocol, supporting secure authentication, pagination, and advanced query capabilities. The solution empowers users to gain actionable insights and master API workflows without deep configuration overhead.

How to Use Dataset Viewer MCP Server: Instant Insights & API Mastery?

  1. Install dependencies: Ensure Python 3.8+ and required libraries (listed in documentation) are installed.
  2. Configure access: Set environment variables for API keys and authentication credentials.
  3. Integrate with tools: Deploy via CLI or integrate directly into development environments like Jupyter notebooks.
  4. Execute operations: Use predefined endpoints for dataset validation, content search, filtering, and statistical analysis.

For enterprise setups, leverage the provided Dockerfile for containerized deployment.

Dataset Viewer MCP Server Features

Key Features of Dataset Viewer MCP Server: Instant Insights & API Mastery?

  • Unified Protocol Access: Leverage the dataset:// scheme for standardized dataset navigation.
  • Smart Pagination: Efficiently manage large datasets through intuitive page-based retrieval.
  • Contextual Search: Perform full-text searches across dataset splits using natural language queries.
  • Advanced Filtering: Apply SQL-like predicates to dynamically segment datasets (e.g., WHERE label = 'positive').
  • Performance Optimization: Caching mechanisms and parallel query execution for high-throughput workflows.

Use Cases of Dataset Viewer MCP Server: Instant Insights & API Mastery?

Common applications include:

  • Data Validation: Verify dataset integrity before model training (e.g., dataset validate imdb_reviews).
  • Rapid Exploration: Quickly browse dataset statistics and sample records without full download.
  • Search-Driven Analysis: Identify patterns in text datasets using keyword-based searches (e.g., "find all entries mentioning 'climate change'").
  • Model Debugging: Filter datasets to isolate edge cases causing model inaccuracies.
  • CI/CD Pipelines: Automate dataset checks as part of machine learning deployment workflows.

Dataset Viewer MCP Server FAQ

FAQ from Dataset Viewer MCP Server: Instant Insights & API Mastery?

How do I handle private datasets?

Use the HUGGINGFACE_TOKEN environment variable to authenticate access to restricted repositories.

Can I use this with legacy Python 2 projects?

No. The server requires Python 3.8+ for modern async HTTP capabilities and security features.

What happens if I exceed API rate limits?

Automatic backoff and exponential retries are implemented, with detailed logging for troubleshooting.

Is there a GUI interface?

While CLI/programmatic access is primary, Jupyter notebook extensions provide visualization capabilities.

Content

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
  1. get_info
* Get detailed information about a dataset
* Parameters: 
  * `dataset`: Dataset identifier
  * `auth_token` (optional): For private datasets
  1. 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
  1. 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
  1. get_statistics
* Get statistics about a dataset split
* Parameters: 
  * `dataset`: Dataset identifier
  * `config`: Configuration name
  * `split`: Split name
  * `auth_token` (optional): For private datasets
  1. 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
  1. 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
  1. 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": [
        "run",
        "dataset-viewer"
      ]
    }
  }
}

Usage Examples

  1. Validate a dataset:
{
  "dataset": "stanfordnlp/imdb"
}
  1. Get dataset information:
{
  "dataset": "stanfordnlp/imdb"
}
  1. Search dataset contents:
{
  "dataset": "stanfordnlp/imdb",
  "config": "plain_text",
  "split": "train",
  "query": "great movie"
}
  1. Filter and sort rows:
{
  "dataset": "stanfordnlp/imdb",
  "config": "plain_text",
  "split": "train",
  "where": "label = 'positive'",
  "orderby": "text DESC",
  "page": 0
}
  1. Get dataset statistics:
{
  "dataset": "stanfordnlp/imdb",
  "config": "plain_text",
  "split": "train"
}

License

MIT License - see LICENSE for details

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