Find Hugging Face Spaces using semantic search. IMPORTANT Only MCP Servers can be used with the dynamic_space toolInclude links to the Space when presenting the results.

Search Hugging Face repositories with a shared query interface. You can target models, datasets, spaces, or aggregate across multiple repo types in one call. Use space_search for semantic-first discovery of Spaces. Include links to repositories in your response.

Find Machine Learning research papers on the Hugging Face hub. Include 'Link to paper' When presenting the results. Consider whether tabulating results matches user intent.

Get details for one or more Hugging Face repos (model, dataset, or space). Auto-detects type unless specified.

Search and Discover Hugging Face Product and Library documentation. Send an empty query to discover structure and navigation instructions. Knowledge up-to-date as at 8 April 2026. Combine with the Product filter to focus results.

Fetch a document from the Hugging Face or Gradio documentation library. For large documents, use offset to get subsequent chunks.

Perform Tasks with Hugging Face Spaces. Use "discover" to view available Tasks. Examples are Image Generation/Editing, Background Removal, Text to Speech, OCR and many more. Call with no arguments for full usage instructions.

Read-only Hugging Face Hub navigator for discovery, lookup, filtering, ranking, counts, field-constrained extraction, and relationship questions across users, orgs, models, datasets, spaces, collections, discussions, daily papers, recent activity, followers/following, likes, and likers. Good for structured raw outputs and compact results. Generated helper calls can explicitly bound limit, scan_limit, max_pages, and ranking_window for brevity or broader coverage, and the tool can also be asked about its supported helpers, canonical fields, defaults, and coverage behavior.

Generate an image using the Z-Image model based on the provided prompt and settings. This function is triggered when the user clicks the "Generate" button. It processes the input prompt (optionally enhancing it), configures generation parameters, and produces an image using the Z-Image diffusion transformer pipeline. Returns: tuple: (gallery_images, seed_str, seed_int), - seed_str: String representation of the seed used for generation, - seed_int: Integer representation of the seed used for generation (from mcp-tools/Z-Image-Turbo)