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

Enables AI-powered image generation using Azure DALL-E 3 and FLUX models with intelligent automatic model selection. Generates stunning photorealistic or creative images directly within LibreChat through simple text prompts.

Turn natural language into 3D models in Fusion 360. 64 CAD tools including sketches, extrudes, fillets,and JIS standard parts.

Enables agents to turn a plain-language description of a device into a complete, validated build package — parts list, 3D-printable parts, wiring, firmware code, picture assembly guide, and tests — that anyone can assemble without electronics experience.

Converts natural language queries into valid GraphQL queries and executes them against GraphQL APIs. Includes schema introspection, query validation, execution with authentication, and query history tracking.

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.

Provides deterministic Unicode 17 text integrity operations—inspection, normalization, comparison, transcoding, security observation, difference explanation, indexing, and protocol profiles—over explicit strings and byte arrays via eight read-only MCP tools.

Enables AI-assisted image generation from text prompts using multiple providers (Tencent Hunyuan, OpenAI DALL-E 3, and Doubao) with support for various artistic styles, resolutions, and negative prompts through a unified interface.

A line-oriented text file editor. Optimized for LLM tools with efficient partial file access to minimize token usage.

Provides tools for AI models to count characters and words in text, supporting English and other space-delimited languages.

Enables users to directly edit authoritative Markdown context and tables within Codex, review the complete text before saving, and commit tracked immutable revisions with version history.

A Model Context Protocol (MCP) server that provides line-oriented text file editing capabilities through a standardized API. Optimized for LLM tools with efficient partial file access to minimize token usage.

Enables local batch image preparation for the web via MCP, including compression, resizing, metadata stripping, thumbnails, and favicon generation.

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.

Gives every AI agent working in a repository a shared, persistent knowledge graph stored in a plain JSONL file, with each entity, relation, and observation tagged with the date and agent that added it. Provides tools and prompts for searching, editing, renaming, merging, cleaning up, and exporting that memory, and keeps it locked to the repository that owns it.

Relays a result screenshot to Slack so you can review a coding agent's output without opening the app.

MCP server for AI agents to send notifications, copy to clipboard, request confirmations, and collect text input from users across their devices.

Routes one brief to the right image model across 60+ (gpt-image-1.5, Ideogram 3, Recraft V4, Flux), validates the output, and fans out to iOS/Android/PWA/favicon/visionOS/Flutter bundles. Works without an API key via Pollinations, HF Inference, Stable Horde, or host-LLM inline SVG.

Enables users to turn any API's documentation into a tested MCP server whose tools expose the API's own operations, with linting, contract tests, and live verification. It accepts OpenAPI/Swagger, Postman, RAML, WSDL, GraphQL, RSS, and HTML docs.

Enables Claude Code to display images inline within supported terminals like Ghostty and Kitty using the Kitty graphics protocol on macOS. It allows users to view various image formats including PNG, JPEG, GIF, and WebP directly in the terminal interface.

Enables turning requirements documents into RFCs, functional test scenarios, and a traceability matrix, with tools to parse, diff, and verify coverage.