Dataset Viewer MCP Server
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- AlicenseAqualityDmaintenanceEnables access to the Hugging Face Hub API to search and retrieve information about machine learning models, datasets, and their metadata. Provides comprehensive tools for exploring the Hugging Face ecosystem including model details, dataset information, and parquet file access.8MIT
- AlicenseAqualityDmaintenanceAn MCP server for the Hugging Face Dataset Viewer API that enables searching, fetching, and filtering datasets on the Hugging Face Hub. It allows users to explore schemas, perform full-text searches, and analyze dataset statistics through natural language.10MIT
- AlicenseNot gradedqualityDmaintenanceProvides direct access to the Hugging Face Hub for searching models and datasets, fetching metadata, and running inference on text, images, and audio.MIT
- AlicenseNot gradedqualityCmaintenanceSearch and retrieve models, datasets, and spaces from Hugging Face Hub. Enables browsing trending items and getting detailed info on repos.182 npmMIT
- AlicenseNot gradedqualityBmaintenanceEnables agents to search and inspect Hugging Face models, datasets, spaces, papers, and documentation, including repository details, file listings, and documentation search/fetch. Runs keyless by default, with optional token support for identity checks, higher rate limits, and private or gated content.172 npmMIT
- AlicenseBqualityCmaintenanceEnables LLMs to search and retrieve read-only information from the Hugging Face Hub about models, datasets, Spaces, papers, and collections. It also supports model comparison and paper summarization prompts.10MIT
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.