"Hugging Face" matching MCP servers:

Automated MCP server that listens for Hugging Face discussion comments, extracts ML tags, and creates pull requests to update model metadata.

Biometric authorization for AI agent actions via Face ID on iPhone, enabling secure approval of sensitive actions and credential-safe API calls through vault execution.

Stdio MCP Server wrapping custom Python runtime (LocalPythonExecutor) from Hugging Faces' smolagents framework. The runtime combines the ease of setup (compared to docker, VM, cloud runtimes) while providing safeguards and limiting operations/imports that are allowed inside the runtime.

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.

A local daemon for macOS that gives any MCP-capable AI client eyes, hands, and a face — screen capture, accessibility tree, mouse/keyboard actions, and an overlay — with a built-in security ceiling.

An MCP server that enables agents to automate refusal direction removal from open-weight LLMs via Optuna-driven search, producing standard Hugging Face models with no inference overhead.

Non-AI deterministic Japanese parser that extracts intents, constraints, references, and execution order into structured Task Packets for safe downstream processing.

Enables AI-powered racing coaching and telemetry analysis for iRacing, providing real-time racing advice, car spotting, lap analysis, and conversational interaction with racing data through live telemetry streaming.

Implements Agentic Context Engineering to create self-improving AI coding assistants that learn from execution feedback and build persistent knowledge playbooks. Reduces token usage by 86.9% while improving code accuracy by 10.6% through incremental context updates.