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Dataset Viewer MCP Server

Dataset Viewer MCP Server

by privetin

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

Related MCP server: HF Dataset MCP

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"
      ]
    }
  }
}

License

MIT License - see LICENSE for details

Available Tools

8 tools
filterB

Filter rows in a Hugging Face dataset using SQL-like conditions

Inputs: dataset (required), config (required), split (required), where (required), +3 more.

get_first_rowsC

Get first rows from a Hugging Face dataset split

Inputs: dataset (required), config (required), split (required), auth_token.

get_infoA

Get detailed information about a Hugging Face dataset including description, features, splits, and statistics. Run validate first to check if the dataset exists and is accessible.

Inputs: dataset (required), auth_token.

get_parquetC

Export Hugging Face dataset split as Parquet file

Inputs: dataset (required), auth_token.

get_rowsC

Get paginated rows from a Hugging Face dataset

Inputs: dataset (required), config (required), split (required), page, +1 more.

get_statisticsC

Get statistics about a Hugging Face dataset

Inputs: dataset (required), config (required), split (required), auth_token.

search_datasetC

Search for text within a Hugging Face dataset

Inputs: dataset (required), config (required), split (required), query (required), +1 more.

validateB

Check if a Hugging Face dataset exists and is accessible

Inputs: dataset (required), auth_token.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    TDQS

    B3.4/5.0

    Scored across 8 tools

    Disambiguation4/5

    Most tools have clearly distinct purposes, such as filter for SQL-like queries, get_info for metadata, and get_parquet for exporting data. However, get_first_rows and get_rows could be slightly confusing as both retrieve rows, though get_rows adds pagination while get_first_rows focuses on initial samples. The descriptions help clarify this distinction, preventing major misselection.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern using snake_case, such as filter, get_first_rows, and validate. This predictability makes it easy for agents to understand and use the tools without confusion over naming conventions.

    Tool Count5/5

    With 8 tools, the server is well-scoped for viewing and interacting with Hugging Face datasets. Each tool serves a specific function, from validation and metadata retrieval to data access and export, providing a comprehensive yet manageable set for the domain.

    Completeness4/5

    The tool set covers core operations for dataset viewing, including validation, metadata retrieval, row access, filtering, searching, and exporting. A minor gap is the lack of tools for modifying or updating datasets, but this aligns with the 'viewer' purpose, and agents can still perform essential read-only workflows effectively.

    Maintenance

    ActivityInactive
    ResponsivenessNo recent community issues

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