Hugging Face is an AI community building the future. They provide tools that enable users to build, train and deploy ML models based on open source code and technologies.
JavaScript implementation of MiniMax MCP that enables interaction with MiniMax AI services for image generation, video generation, text-to-speech, and voice cloning through MCP-compatible clients.
Last updated
4
10
335
125
MIT
Why this server?
Connects to MiniMax's Hugging Face organization to access related models and resources
Enables MCP clients like Claude Desktop and Cursor to interact with MiniMax APIs for generating speech, cloning voices, creating videos, and generating images.
Last updated
6
1,517
MIT
Why this server?
Integrates with Hugging Face APIs to access the TSpec-LLM dataset containing 535M words of 3GPP specification data for direct specification search and analysis
Enables AI assistants to access and search 3GPP telecommunications specifications through direct integration with the TSpec-LLM dataset. Provides real-time specification content, implementation requirements, and multi-spec comparisons for 3GPP standards development.
Last updated
4
4
44
26
MIT
Why this server?
Uses Hugging Face's sentence transformers API to generate embeddings for semantic search in the RAG system, specifically leveraging the sentence-transformers/all-MiniLM-L6-v2 model for document and memory vectorization
A complete MCP server for Retrieval-Augmented Generation with file management and vector memory for agents. Supports multiple document formats (PDF, DOCX, TXT, MD, CSV, JSON) with semantic search using Hugging Face embeddings and ChromaDB for efficient vector storage.
Last updated
11
6
1
MIT
Why this server?
Enables interaction with various open-source AI models hosted on Hugging Face through the unified LiteLLM interface
Provides unified access to 100+ AI models from OpenAI, Anthropic, Google, AWS Bedrock and other providers through a single MCP interface. Enables seamless switching between different AI models using LiteLM's unified API without requiring separate integrations for each provider.
Last updated
3
8
MIT
Why this server?
Downloads FLUX models and related AI models from Hugging Face repositories for image generation workflows.
Enables AI image generation using FLUX models through ComfyUI with GPU acceleration, supporting image generation, 4x upscaling, and background removal with optimized Docker deployment.
Last updated
6
4
MIT
Why this server?
Provides a similar user experience to Hugging Face for model access and deployment workflows
Provides tools for evaluating and benchmarking AI explanation methods through a standard interface that can be used with AI assistants and MCP-compatible applications.
Last updated
11
MIT
Why this server?
Provides local semantic embeddings using models from Hugging Face (e.g., Xenova/all-MiniLM-L6-v2) for memory search and injection.
MCP Extension that gives LLMs access to arXiv and Hugging Face papers, enabling users to discuss papers, search for new research, and organize literature reviews through natural conversation.
