Dataset Viewer MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| 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. |
| get_rowsC | Get paginated rows from a Hugging Face dataset |
| get_first_rowsC | Get first rows from a Hugging Face dataset split |
| search_datasetC | Search for text within a Hugging Face dataset |
| filterB | Filter rows in a Hugging Face dataset using SQL-like conditions |
| get_statisticsC | Get statistics about a Hugging Face dataset |
| get_parquetC | Export Hugging Face dataset split as Parquet file |
| validateB | Check if a Hugging Face dataset exists and is accessible |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| analyze-dataset | Analyze a dataset's content and structure |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
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.