"Hugging Face" matching MCP servers:

Enables LLMs to search and retrieve read-only information from the Hugging Face Hub about models, datasets, Spaces, papers, and collections. It also supports model comparison and paper summarization prompts.

Provides MCP tools for scanning, redacting, and packaging dataset/model directories for public release, leveraging Presidio for PII detection and generating Hugging Face cards and EU AI Act training-data summaries.

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.

AgentAvow scans any MCP server, package (npm/PyPI/crates/Docker/Hugging Face), or GitHub repo and returns a signed, offline-recomputable 0–100 trust score with a plain safe / needs-review verdict. Authless and read-only, no account.

Enables AI agents to search, compare, and right-size Hugging Face models, including estimating whether a model fits in available GPU VRAM before downloading it.

Enables MCP-compatible AI agents to run structured searches over India's court judgments, tribunal matters, and legislation directly from Hugging Face parquet files, with automatic license attribution.

Enables MCP clients to drive a LEGO Mindstorms EV3 over Wi-Fi SSH, with tools for movement, turning, stopping, beeping, and listing connected devices. Includes a CLI chat client that translates natural language into robot commands via Hugging Face Inference Providers.

Enables generating game assets — images, 3D meshes, sound and music — through free Hugging Face Spaces with no GPU or subscription, plus procedural synthesizers that consume no quota at all. Adds licence gating, per-take provenance metadata, and post-processing such as audio trimming/looping/normalising and six-angle Blender mesh renders.

Provides access to over 100 curated AI agent skills from organizations like Anthropic, Vercel, and Hugging Face. It enables AI assistants to perform specialized tasks including document processing, security analysis, and web development through a standardized interface.

Semantic code search for any MCP client: indexes local folders of code and docs and answers plain-language or code queries with matching passages, file paths, line ranges and scores. Embedding runs on the local CPU with the Motherlode code-search encoder, so no file content leaves the machine; the encoder downloads once from Hugging Face. Install: npx -y blackwindow-weave-mcp.

Tell your AI agent what you want a model to do. It searches Hugging Face for each part of the job and tells you which parts already exist there: which are an exact fit, which to fine-tune, which to train, and which are just code. Every model it names is real, current, and sized for your hardware.

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.

MCP server for the abliteration.org catalog: 18 read-only tools over every abliterated and uncensored LLM on Hugging Face, with method classification, download trends, deletion history, and four alignment indices.

Enables searching and gathering information from multiple online sources (web, Twitter/X, Telegram, GitHub, Hugging Face, arXiv) through a unified MCP interface.

Supercharges Claude Desktop with persistent semantic memory, sandboxed file I/O, live web search, and local emotional intelligence using a local ChromaDB and Hugging Face model.

Enables plain-English description of training data needs to search Hugging Face and GitHub, composing coverage-scored training packs without API keys or Bridge.

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

Enables users to scan Hugging Face for Apple-MLX models, estimate memory fit and decode speed for their Mac, and get ranked recommendations for a use case, with all agent work running locally.

An MCP server for the Hugging Face Dataset Viewer API that enables searching, fetching, and filtering datasets on the Hugging Face Hub. It allows users to explore schemas, perform full-text searches, and analyze dataset statistics through natural language.

MCP server that brings AI paper reading and code repository discovery from Hugging Face Papers into any MCP-compatible client.