Dataset Viewer MCP Server
# Dataset Viewer MCP Server
An MCP server for interacting with the [Hugging Face Dataset Viewer API](https://huggingface.co/docs/dataset-viewer), 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](https://github.com/astral-sh/uv) - Fast Python package installer and resolver
### Setup
1. Clone the repository:
```bash
git clone https://github.com/privetin/dataset-viewer.git
cd dataset-viewer
```
2. Create a virtual environment and install:
```bash
# 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`
```json
{
"mcpServers": {
"dataset-viewer": {
"command": "uv",
"args": [
"--directory",
"parent_to_repo/dataset-viewer",
"run",
"dataset-viewer"
]
}
}
}
```
## License
MIT License - see [LICENSE](LICENSE) for detailsTDQS
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.