Loading ToolBench
MCP server for HuggingFace Hub providing tools to search models, retrieve model information, and read files from HuggingFace repositories
The server provides 5 tools with reasonable descriptions and parameter coverage, but has several quality gaps that prevent a higher score. All tools have descriptions in the 100-200 character range, which is appropriate. However, parameter schemas lack explicit type constraints (enums for repo_type, no min/max for numeric bounds), and the tools expose a low-level 'headers' dict parameter that should be abstracted away. Error handling is minimal, there is no guidance for what to do when a model is not found or when authentication fails. The composition is reasonable (5 focused read/search tools), but several tools are near-duplicates (read_config_json vs read_config both read config; read_file can read any file including config). Output schemas are not documented in the code. Token injection via headers.get('hf-token') is correct (not exposing as a parameter), but the headers dict parameter itself is a minor leak of transport details.
Get detailed information about a specific model from HuggingFace Hub. This tool retrieves comprehensive metadata about a model repository including model configuration, files, tags, author information, and other relevant details. Useful for understanding model capabilities, architecture, and usage requirements.
Read and parse the config.json file from a HuggingFace repository. This tool reads the config.json file from a model repository and parses it into a structured format. The config.json file contains essential model information including architecture type, hyperparameters, model dimensions, and other configuration settings. This is particularly useful for understanding model specifications and compatibility requirements.
Read the raw config.json file from a HuggingFace repository. This tool specifically reads the config.json file from a model repository, which contains the model's configuration parameters, architecture details, and metadata. The content is returned as raw JSON text, which can be useful for examining the exact configuration format.
Read a specific file from a HuggingFace repository. This tool allows you to access and read the contents of any file within a HuggingFace repository, including model files, configuration files, documentation, and other assets. Useful for examining model configurations, code, weights, and other repository contents.
Parameter 'repo_type' lacks enum constraint. Accepts 'model', 'dataset', 'space' but no validation declared in schema. LLM may pass invalid values like 'repository' or 'space_model'.
Tool 'headers' parameter exposed to LLM is a transport-level detail. Should be abstracted via server-side token injection. Currently relies on LLM to pass 'hf-token' header correctly.
No output schemas documented. Tool descriptions state what is returned (e.g., 'ModelSearchResponse', 'ReadFileResponse') but the code does not show the structure of these response objects. LLM cannot plan downstream calls without knowing response fields.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | D | 57 | <=2025-11-25 | v2 |
| 2026-03-09 | D | 50 | - | v1 |
Search for models on HuggingFace Hub using a query string. This tool allows you to search for models, datasets, and spaces on the HuggingFace Hub. The search returns a list of matching repositories with their metadata including model names, authors, downloads, likes, and other relevant information.
read_config_json and read_config are near-duplicates. Both read config.json from a repo; one returns raw JSON text, the other parses it. This creates ambiguity for LLM tool selection. Recommend consolidating into a single tool with an optional 'parse' parameter or two clearly distinct names.
No error handling guidance. Descriptions do not explain what to do if a model is not found, if authentication fails, or if a file is missing. Error responses should guide recovery (e.g., 'Model not found. Try search_model() with a partial name.').
Parameter 'revision' defaults to 'main' but no constraint on format. Could accept git hashes, branch names, or tags. Description hints at this ('branch name, tag, or commit hash') but schema provides no validation or length bounds.
Parameter 'search' in search_model lacks constraints. No min/max length, no guidance on query syntax or best practices. LLM may pass empty strings or extremely long queries.