Dataset Viewer MCP Server
This server allows browsing, analyzing, searching, filtering, and retrieving data from Hugging Face datasets.
Browse datasets: Access datasets using the
dataset://URI scheme, including configurations and splits.Validate datasets: Check if a dataset exists and is accessible.
Get detailed info: Retrieve dataset descriptions, features, splits, and statistics.
Paginate rows: Fetch dataset contents in paginated chunks.
Fetch first rows: Retrieve the initial rows from a dataset split.
Extract statistics: Analyze dataset statistics for splits.
Search dataset: Find text within a dataset using search queries.
Filter rows: Apply SQL-like conditions to filter dataset rows.
Download datasets: Export entire datasets in Parquet format.
Handle authentication: Access private datasets using Hugging Face authentication tokens.
Allows interaction with the Hugging Face Dataset Viewer API, providing tools for browsing, searching, filtering, and analyzing datasets hosted on the Hugging Face Hub, along with support for authentication for private datasets.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Dataset Viewer MCP Server show me the first 10 rows from the imdb dataset"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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 datasetsSupports 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:
validate
Check if a dataset exists and is accessible
Parameters:
dataset: Dataset identifier (e.g. 'stanfordnlp/imdb')auth_token(optional): For private datasets
get_info
Get detailed information about a dataset
Parameters:
dataset: Dataset identifierauth_token(optional): For private datasets
get_rows
Get paginated contents of a dataset
Parameters:
dataset: Dataset identifierconfig: Configuration namesplit: Split namepage(optional): Page number (0-based)auth_token(optional): For private datasets
get_first_rows
Get first rows from a dataset split
Parameters:
dataset: Dataset identifierconfig: Configuration namesplit: Split nameauth_token(optional): For private datasets
get_statistics
Get statistics about a dataset split
Parameters:
dataset: Dataset identifierconfig: Configuration namesplit: Split nameauth_token(optional): For private datasets
search_dataset
Search for text within a dataset
Parameters:
dataset: Dataset identifierconfig: Configuration namesplit: Split namequery: Text to search forauth_token(optional): For private datasets
filter
Filter rows using SQL-like conditions
Parameters:
dataset: Dataset identifierconfig: Configuration namesplit: Split namewhere: SQL WHERE clause (e.g. "score > 0.5")orderby(optional): SQL ORDER BY clausepage(optional): Page number (0-based)auth_token(optional): For private datasets
get_parquet
Download entire dataset in Parquet format
Parameters:
dataset: Dataset identifierauth_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
Clone the repository:
git clone https://github.com/privetin/dataset-viewer.git
cd dataset-viewerCreate 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 toolsfilterB
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
Scored across 8 tools
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.
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.
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.
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
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