image-gen3-google-mcp-server
Related Servers
Alternatives to image-gen3-google-mcp-server
No user-submitted related servers found.
Related Servers
- FlicenseBqualityDmaintenanceGenerates high-quality images using Google's Imagen 3.0 model via the Gemini API with support for up to four images per request. It provides automated file management and creates HTML previews for seamless image viewing within MCP-compatible hosts.24-
- AlicenseNot gradedqualityDmaintenanceEnables generating, editing, and manipulating images using Google Gemini Flash 2.5 through natural language prompts. Supports text-to-image generation, image editing, multi-image composition, and batch processing with direct file management.66 npm4MIT
- AlicenseAqualityCmaintenanceEnables AI image generation and editing using Google's Gemini Multimodal Image APIs.61MIT
- AlicenseAqualityDmaintenanceEnables image generation, editing, and analysis using Google's Gemini 2.5 Flash and Gemini 3 Pro models, with support for batch processing, style templates, and high-resolution output.81,016 npm1MIT
- AlicenseBqualityDmaintenanceEnables image generation using Google Gemini models like Gemini 2.0 Flash and Imagen 3.0 with support for custom aspect ratios and negative prompts. It also allows users to list and manage generated images stored in local directories.27 npmMIT
- FlicenseNot gradedqualityDmaintenanceGenerate high-quality images from text descriptions using Google's Imagen 4.0 models with multiple quality variants, flexible aspect ratios, and local file storage.3-
TDQS
Scored across 2 tools
Each tool targets a completely different operation: generate_images creates new images via AI, while create_image_html outputs HTML for existing image files. There is no ambiguity or overlap between them.
Both tools follow a clear verb_noun pattern in snake_case. 'generate_images' and 'create_image_html' use consistent styling and predictable naming conventions.
Only 2 tools for an image generation service is borderline. While the core functionality (generation and HTML output) is covered, the small number suggests a limited scope that may require additional tools for a complete workflow.
The tool set is notably incomplete: it lacks tools for listing, deleting, or managing generated images, and there is no way to configure generation parameters beyond what might be in the description. Users are left with a generation-and-output loop without lifecycle management.