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Glama

Huggingface

by io.github.pipeworx-io

List Model Files

list_model_files
Read-onlyIdempotent

Hugging Face Hub API — list the files in a model repository at a given revision (branch, tag or commit; main by default) and optional subdirectory path. Returns each entry's path, type, byte size and blob SHA, including LFS details for weight shards. Answers which weight, config and tokenizer files ship in a Hugging Face model repo and how large they are.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathNoSubdirectory (default root)
repo_idYes
revisionNoBranch/commit/tag (default main)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed2 schema fields changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "repo_id": "bert-base-uncased"
+  },
+  {
+    "path": "config",
+    "repo_id": "meta-llama/Llama-2-7b"
+  }
+]
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "description": "List of files in a model repository",
+  "type": "object"
+}
  2. First observed

TDQS

A4.5/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover read-only, idempotent, non-destructive behavior, so the bar is lower. The description adds transparency about return content: path, type, byte size, blob SHA, and LFS details for weight shards. It also clarifies revision defaults (main) and subdirectory behavior, adding useful behavioral context 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with the core action and API context. The first sentence states the operation and parameters; the second details return fields and the use case. No filler or redundant explanation; every sentence contributes.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a moderate-complexity listing tool with an output schema and strong annotations, the description covers what is done, how parameters behave, what is returned, and the typical use case. No significant gaps for an agent to select and invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 67% (revision and path have descriptions, repo_id does not). The description adds semantic value by explaining revision as 'branch, tag or commit; main by default' and path as 'optional subdirectory path.' Repo_id is not explicitly described but implied by 'model repository' and examples, compensating reasonably.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('list') and resource ('files in a model repository') with clear scope: at a given revision and optional subdirectory. It distinguishes from siblings like list_dataset_files by focusing on model repos and even mentions LFS details for weight shards, making it unmistakably specific.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description states the use case explicitly: 'Answers which weight, config and tokenizer files ship in a Hugging Face model repo and how large they are.' This tells the agent when to use it, and the sibling list_dataset_files implies the alternative for datasets, though no direct exclusion is stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.5/5.0
Disambiguation3/5

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 Consistency2/5

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.

Tool Count2/5

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.

Completeness3/5

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.

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