"Hugging Face" matching MCP servers:

An MCP server that provides Hugging Face Hub API and Search endpoints through multiple transport protocols (STDIO, SSE, StreamableHTTP, and StreamableHTTPJson), enabling integration with AI model capabilities.

Enables interaction with the Hugging Face Dataset Viewer API, allowing users to browse, search, filter, and analyze datasets hosted on the Hugging Face Hub.

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.

Enables MCP-capable assistants to quantize Hugging Face models into GGUF format and publish the results to your Hugging Face profile, with tools for credits, pricing, model browsing, and job status.

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.

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.

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.

Provides various utilities including timezone-aware time queries, basic calculator operations, multilingual greetings, code review prompts, and AI image generation through Hugging Face API.

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.

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

Enables safety-ranked discovery of open-source components across npm, PyPI, crates.io, RubyGems, GitHub, and Hugging Face, with license, security, and health checks.

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 management of local video datasets with directory browsing and direct upload capabilities to Hugging Face Hub repositories. Supports video file validation, batch uploads, and automated dataset repository creation with secure authentication.

Provides AI agents with 6 tools for searching Hugging Face models, GitHub trending repos, analyzing GitHub repositories, fetching dev.to articles, Show HN launches, and Product Hunt daily launches. Built for dev tooling research and AI ecosystem analysis.

Enables AI assistants to generate realistic, synthetic test data on demand, including valid PESELs, NIPs, addresses, and more across 27 EU countries, through two tools: list_generators and generate.

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.

Enables AI agents to execute server-side JavaScript and perform CRUD operations directly on ServiceNow instances with context bloat reduction features for efficient token usage.

Enables LLM agents to author EisenScript procedural grammars and render them headlessly into Wavefront OBJ meshes, returning vertex/face counts and bounding boxes alongside validation, linting, PNG previews, mesh-format conversion, batch gallery rendering, and Unreal import.