"Hugging Face" matching MCP servers:

An unofficial MCP server that provides semantic search capabilities for Hugging Face models and datasets, enabling Claude and other MCP-compatible clients to search, discover, and explore the Hugging Face ecosystem using natural language queries.

An MCP server that turns the Hugging Face Hub into a conversational model picker with a tool-use eval harness to prove the agent actually uses it correctly.

Enables AI assistants to fetch sourced, citable face measurement percentiles, face type definitions and threshold rules, methodology and citation details, and to classify a face shape from four distances using the site's seven rules in order. Read-only and takes no photo.

Enables face keypoint detection, beautification, age/gender transform, face swap, cartoonization, and liveness detection via AceDataCloud API.

Checks whether an open-weight LLM fits your GPU: VRAM for weights, KV cache and overhead at each quantisation, a tokens-per-second ceiling, the longest context that fits, and what would work instead when it doesn't. Reads any Hugging Face repo's config.json. Free, read-only, no API key.

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.

Enables AI agents to search and read Mexican federal, state and municipal legislation, retrieving the current text or the version in force on any past date, comparing reform versions, tracing amendment history and returning official links for citation. It runs locally or as an optional HTTP server, pulling the search index from Hugging Face and text and history from a public GitHub repository, with optional live extraction from official portals.

Generates realistic human face images that don't represent real people, offering various output shapes, configurable dimensions, and batch generation capabilities.

Exposes safe JSON contracts for validating and preparing datasets, running supervised fine-tuning, preference learning, RL, distillation, and evaluation, and managing checkpoints and exports through the Tinker SDK and Cookbook. It lets agents discover models and Hugging Face datasets, plan and execute training recipes, compare experiments, and export selected models.

Gives Claude Code, Cursor and other agents structural awareness of a PyTorch model: layers, params, FLOPs, blast radius, the design linter, a full readiness/cost/deployment verdict, and a ranker for which of k candidate designs to train. Reads a .py, a .neurarch.json, 81 bundled reference architectures, or a Hugging Face repo. Offline, no API key.

Provides multi-provider image generation with automatic fallback across services like Pollinations.ai, Cloudflare, and Hugging Face. It features specialized pixel art generation, cost tracking, and automatic saving of generated visual assets to disk.

A local RAG server that enables document indexing and sentence window retrieval across multiple file formats like PDF, MD, and DOCX. It supports both local Hugging Face models and OpenAI embeddings for efficient context-aware querying through the Model Context Protocol.

A boilerplate for building and deploying MCP servers on Vercel, featuring example tools like greeting, calculator, weather, and Hugging Face image generation.

Enables AI agents to import datasets from Hugging Face or Parquet, CSV, JSON, JSONL and DuckDB sources, map them onto a user-defined schema with per-row metadata, then preview, sample and write the converted rows into DuckDB files. Also exposes schema management, split lookup and target listing so an entire conversion pipeline can run in one call.

Connects LLMs to the Hugging Face Hub and thousands of Gradio AI applications, enabling repository search, file access, Space interaction, jobs, and sandbox tools through MCP. Supports STDIO and Streamable HTTP transports with optional Hugging Face authentication.

A boilerplate project for quickly developing Model Context Protocol (MCP) servers using TypeScript SDK, featuring example tools (calculator, greet), resources (server info), and AI image generation capabilities via Hugging Face.

Enables searching, fetching, and analyzing scientific papers from ArXiv, ACL Anthology, Semantic Scholar, and Hugging Face datasets, with optional LLM-powered document QA and research proposal workflows.

FaceTron is a high-performance face embedding server with ONNX Runtime, dynamic multi-model loading, and MCP-compatible OpenAPI metadata for offline face recognition and detection.

Enables agents to download single model weight files from Hugging Face repositories or Civitai model versions directly into allowlisted ComfyUI model folders. Source identifiers, destination categories, and file extensions are validated, and existing files are never overwritten.

An MCP server that generates 2D and 3D game assets from text prompts using AI models from Hugging Face Spaces, allowing developers to easily create game art through Claude Desktop or other MCP clients.