"Using Hugging Face for Text-to-Audio, Image, and Video Generation" matching MCP servers:

Enables access to the Hugging Face Hub API to search and retrieve information about machine learning models, datasets, and their metadata. Provides comprehensive tools for exploring the Hugging Face ecosystem including model details, dataset information, and parquet file access.

A Model Context Protocol server that provides Claude and other LLMs with read-only access to Hugging Face Hub APIs, enabling interaction with models, datasets, spaces, papers, and collections through natural language.

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.

Translates plain English questions about infrastructure operations into SQL queries, executes them against a database, and returns the real answer.

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Enables transcription of audio files using Google Gemini AI and creates a searchable knowledge base, allowing users to ask natural language questions about content in meetings, podcasts, lectures, and other audio recordings.

Enables AI assistants to search and retrieve structured video material, including dialogue, jokes, story structure, and video details through natural language queries.

Enables AI agents to generate duration-aware, fact-checked video scripts from topics or transcripts by exposing tools for live news/web research, article fetching, and transcript ingestion, plus resources and style prompt templates.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

Enables asking natural language questions about local videos, using Whisper transcription, ChromaDB vector storage, and Groq for timestamped answers via MCP.

Enables searching and retrieving documentation from crawled documentation sites as an MCP server, allowing coding agents to query real docs instead of relying on training data.

A tool that creates MCP (Model Context Protocol) servers from OpenAPI/Swagger specifications, enabling AI assistants to interact with your APIs.

Builds searchable SQLite databases from PDFs, preserving inline image locations for AI agents to discover and caption visual content. Supports full-text search over text, image placeholders, and saved captions.

Enables LLMs to query documents using semantic search, supporting PDFs, Word, Excel, and more. Organizes documents by topics from folder structure and provides advanced search features like phrase matching and date filtering.

Enables searching Bilibili videos by question and retrieving specific timestamped segments that answer it, with direct jump-back links. It performs local transcription, caching, and Chinese BM25 ranking to provide precise video evidence without external APIs.

A Cloudflare Worker that transforms Cloudflare AI Search (AutoRAG) instances into an MCP server for querying documentation. It enables AI models to search and retrieve relevant information from custom document sets stored in R2 buckets.

Exposes any SQLite database as read-only MCP tools for AI assistants, enabling listing tables, describing schemas, and running SELECT queries with filtering, ordering, and pagination.

Enables natural language search through personal video collections, returning AI-generated descriptions and direct URLs to relevant 30-second video clips.

An Operit-compatible adapter of the Exa MCP server, enabling web search, code search, and company research capabilities in AI assistants. It fixes MCP handshake compatibility issues, allowing tools like web_search_exa and web_fetch_exa to load and run reliably.

Enables AI agents to fetch one or many web pages concurrently and receive clean, LLM-ready Markdown with navigation, ads and boilerplate stripped away, plus extracted metadata such as title, description, author, Open Graph tags and canonical URL. It runs locally over stdio with no API key or account, requiring only a single command.