list_dataset_filesList files in a Hugging Face dataset repository by repo_id and optional revision/subdirectory path; returns filename, size, and blob SHA for each file.
| Name | Required | Description | Default |
|---|---|---|---|
| path | No | ||
| 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"
+ },
+ {
+ "path": "data",
+ "repo_id": "openwebtext/openwebtext"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "description": "List of files in a dataset repository",
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, non-destructive. Description adds return value details (filename, size, blob SHA) and optional parameters (revision, path), providing useful behavioral context beyond annotations. No contradictions.
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, 22 words, directly states purpose and key parameters. No filler or redundancy, perfectly front-loaded.
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?
With output schema present, description need not detail return format but does so helpfully. Covers repo_id, revision, path. Lacks mention of pagination or limits, but for a list tool this is acceptable.
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 coverage is 0% so description must compensate. It names repo_id as required and mentions revision and path as optional, but does not explain their formats or constraints. Examples partially clarify path as subdirectory, but detail lacking.
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?
Clearly states the tool lists files in a dataset repository, with specific parameters (repo_id, revision, path) and return fields (filename, size, blob SHA). Distinguishes from sibling 'list_model_files' by scope (dataset vs model).
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 indicates usage scenario (list files by repo_id and optional revision/path) but does not explicitly state when not to use or mention alternatives like 'list_model_files'. Guidance is implied but not explicit.
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.