"How to create draft emails using Gmail's support agent class" matching MCP servers:

MCP server for generating images using Gemini, supporting multiple aspect ratios and both AI Studio and Vertex AI backends.

An MCP server that generates and edits images using OpenAI's GPT Image model, allowing users to create images from text descriptions and edit existing images through natural language.

A local MCP server that compiles structured editing plans with real media paths and time ranges into validated, editable Jianying drafts. It provides tools for probing media, validating plans, creating drafts, checking status, and installing them for further editing.

Enables AI assistants to convert CSS-animated SVGs into videos (MP4/WebM/MKV/MOV) and animated images (aPNG/GIF) with frame-accurate rendering and transparency support.

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 LLM agents to process local videos into timestamped, citable text documents and then query them through tools for listing videos, retrieving transcripts, and fetching specific segments, all fully offline.

Enables non-vision LLMs to analyze images via any OpenAI-compatible vision API. Hardened against truncation, empty responses, and timeouts for reliable analysis.

A lightweight MCP server that enables text agents to analyze images and videos using OpenAI-compatible vision models, with tools for image analysis and video frame extraction.

Provides local vision and audio perception for MCP-compatible agents, enabling them to read images, transcribe text from visual media, analyze videos, and convert speech to text entirely on-device. It is privacy-focused with no cloud upload or API keys by default, using Ollama for inference.

Enables AI agents to operate GIMP 3 end-to-end: open and inspect images, call every PDB procedure, apply GEGL filters destructively or as layer effects, measure pixels, render before/after/diff comparisons, cut out subjects with AI segmentation, and run multi-step recipes across folders.

Enables local-first creation, editing, and animation of pixel-art Aseprite documents through MCP stdio, with atomic previews, canvas drawing, layers, frames, palettes, tags, slices, tilemaps, and cast tools. It also supports strict asset validation and verified Phaser/game-asset export from reopened native masters.

Enables LLM clients to access crop yield predictions from RGB imagery, topic-based PDF literature, and soil lab/moisture CSV data.

Enables AI agents to find meme and reaction clips via the Vlipsy API or local folders, look at them as contact-sheet images, and fetch or convert them into rendered assets with attribution sidecars. It also drops those clips into ffmpeg renders and editing timelines (OTIO, FCPXML, Premiere XML, EDL) or returns paste-ready Remotion, HyperFrames, MoviePy and HTML snippets.

MCP server for ingesting UI screenshots, Figma MCP node JSON, or hybrid Figma-plus-screenshot sources into a cross-platform design-to-code artifact pipeline.

Enables AI agents to drive a live Blender session over stdio, inspecting the scene graph, searching and calling operators, reading and writing RNA properties, rendering viewable images, and checkpointing or restoring the file across a multi-turn conversation. It also exposes gallery and scene-reset helpers so the agent can check its own work and start over cleanly.

Enables applications and AI agents to inspect, convert, extract, and process media through asynchronous HTTP and MCP tasks, with optional natural-language planning and A2A interoperability.

MCP server for generating print-ready 3D models from text descriptions via a multi-agent pipeline, enabling AI assistants to create and optimize STL files.

Enables text-to-image generation using Zhipu AI's CogView-4 API. Supports generating images from text prompts with configurable size and quality parameters through MCP-compatible clients like Claude Desktop and Cline.

Enables AI agents to inspect and control a running WARUDO scene, including assets, character pose and IK, expressions, blueprints, camera, and captures, through an MCP client.

Enables MCP clients to generate images from text prompts and edit existing PNG/JPEG files with natural-language instructions by driving a local ComfyUI instance running Qwen Image models. Supports aspect ratios, exact pixel dimensions, batch counts, and reproducible seeds, with matching capabilities exposed through a Python CLI.