Content
hf-mcp-server 8
Hugging Face MCP Server
hf_whoami
Hugging Face tools are being used by authenticated user 'chenwnejia'
No parameters required
space_search
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.
Semantic Search Query
Number of results to return
Only return MCP Server enabled Spaces
hub_repo_search
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.
Search term. Leave blank and specify sort + limit to browse trending or recent repositories.
Repository types to search. Defaults to ["model", "dataset"]. space uses keyword search via /api/spaces.
Organization or user namespace to filter by (e.g. 'google', 'meta-llama', 'huggingface').
Optional hub filter tags. Applied to each selected repo type (e.g. ["text-generation"], ["language:en"], ["mcp-server"]).
Sort order (descending): trendingScore, downloads, likes, createdAt, lastModified
Maximum number of results to return per selected repo type
paper_search
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.
Semantic Search query
Number of results to return
Return a 2 sentence summary of the abstract. Use for broad search terms which may return a lot of results. Check with User if unsure.
hub_repo_details
Get details for one or more Hugging Face repos (model, dataset, or space). Auto-detects type unless specified.
Repo IDs for (models|dataset/space) - usually in author/name format (e.g. openai/gpt-oss-120b)
Specify lookup type; otherwise auto-detects
hf_doc_search
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 1 April 2026. Combine with the Product filter to focus results.
Start with an empty query for structure, endpoint discovery and navigation tips. Use semantic queries for targetted searches.
Filter by Product. Supply when known for focused results
hf_doc_fetch
Fetch a document from the Hugging Face or Gradio documentation library. For large documents, use offset to get subsequent chunks.
Documentation URL (Hugging Face or Gradio)
Token offset for large documents (use the offset from truncation message)
gr1_z_image_turbo_generate
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)
Text prompt describing the desired image content
Output resolution in format "WIDTHxHEIGHT ( RATIO )" (e.g., "1024x1024 ( 1:1 )")
Seed for reproducible generation
Number of inference steps for the diffusion process
Time shift parameter for the flow matching scheduler
Whether to generate a new random seed, if True will ignore the seed input
everything-claude-code
markitdown
firecrawl
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servers