get_datasetDetailed Hugging Face dataset metadata by repo_id (e.g. "squad") and optional revision; returns card data, tags, downloads, task categories, and file list.
| Name | Required | Description | Default |
|---|---|---|---|
| repo_id | Yes | ||
| revision | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "repo_id": "wikitext"
+ },
+ {
+ "repo_id": "openwebtext/openwebtext",
+ "revision": "main"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "description": "Detailed dataset repository information",
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint; description adds specific return fields (card data, tags, downloads, task categories, file list). No contradiction, and description enhances understanding beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence of 20 words, front-loaded with purpose. No superfluous information, every word serves the explanation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given rich annotations and existence of output schema (though not shown), description covers key output fields. Missing default revision behavior and error cases, but sufficient for a simple lookup tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% description coverage. Description mentions repo_id with example 'squad' and optional revision but does not provide format details, constraints, or default values. Minimal added meaning beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states verb 'Get', resource 'dataset metadata', and specific outputs (card data, tags, downloads, etc.). Distinguishes from sibling tools like search_datasets and list_dataset_files by focusing on single dataset metadata retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description implies usage when detailed metadata for a specific dataset is needed. Does not explicitly state when not to use or provide direct comparisons to siblings, but context from sibling list aids inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
The tool set includes many similarly-named tools, especially the 'ask_pipeworx' variants and the multiple polymarket tools, which could cause confusion. However, each tool has a detailed description specifying its unique purpose, so an agent reading carefully can distinguish them.
Naming is inconsistent across the set: Huggingface tools use 'get_', 'list_', 'search_' prefixes, while Pipeworx tools use varied verbs like 'ask_pipeworx', 'bet_research', 'entity_profile', and others. There is no overall pattern or convention, making it harder to predict tool names.
With 41 tools, the server is heavily loaded. While each tool has a distinct role, the scope combines two large domains (Huggingface and Pipeworx), leading to a tool count well above the typical 3-15 range for a focused server.
The server covers a wide range of functionalities: Huggingface model/dataset queries, Pipeworx data lookups, subscription management, and memory tools. However, it lacks write operations for Huggingface (e.g., uploading models/datasets) and some lifecycle operations, leaving noticeable gaps.