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Glama

Huggingface

by io.github.pipeworx-io

Search Models

search_models
Read-onlyIdempotent

Search Hugging Face Hub models with filters for author, library (transformers/diffusers/…), language, pipeline_tag (text-generation/…), and tags; sort by downloads, likes, or trendingScore.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fullNoInclude extra fields (cardData, gated, etc.)
sortNodownloads | likes | trendingScore | lastModified | createdAt
tagsNoComma-separated tags
limitNo1-1000 (default 20)
authorNoFilter by org or user (e.g. "meta-llama")
searchNoFree-text — name / description
libraryNotransformers | diffusers | sentence-transformers | ...
languageNoISO language code, e.g. "fr" — matched as a Hub tag, not validated
directionNo-1 (desc, default) | 1 (asc)
pipeline_tagNotext-generation | text-classification | image-classification | translation | ...

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
    • changedInput schema / properties / language / description
      Previous value: -"ISO language code"New value: +"ISO language code, e.g. \"fr\" — matched as a Hub tag, not validated"
    • changedInput schema / properties / sort / description
      Previous value: -"downloads | likes | trending_score | lastModified | createdAt"New value: +"downloads | likes | trendingScore | lastModified | createdAt"
  2. Changed2 schema fields changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "library": "transformers",
+    "limit": 20,
+    "search": "bert"
+  },
+  {
+    "author": "meta-llama",
+    "direction": "-1",
+    "limit": 10,
+    "pipeline_tag": "text-generation",
+    "sort": "downloads"
+  }
+]
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "description": "List of models matching search criteria",
+  "type": "object"
+}
  3. First observed

TDQS

A3.9/5.0
Behavior3/5

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

Annotations already provide readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds that it searches and returns results with filters, but does not disclose additional behavioral traits beyond what annotations already cover.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

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

The description is a single concise sentence that packs many details. It is efficient, though slightly dense. Could be restructured for readability, but overall well-sized.

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

Completeness4/5

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

Given the tool's medium complexity (10 parameters) and the presence of an output schema and annotations, the description covers the main purpose and key filters. It doesn't detail pagination or the 'direction' parameter's relationship to sorting, but is reasonably complete.

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

Parameters3/5

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

Schema coverage is 100% with all 10 parameters described in the schema. The description lists some filters and sorting but adds little meaning beyond the schema, so baseline 3 is appropriate.

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 clearly states it searches Hugging Face Hub models with specific filters (author, library, language, pipeline_tag, tags) and sorting options. It distinguishes this tool from siblings like search_datasets and search_spaces.

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 implies when to use this tool (searching models), but does not explicitly state when not to use or compare to alternatives. Among siblings, it's clear it's for models, but lacks explicit exclusion guidance.

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.

Resources