A
licenseA
qualityD
maintenanceEnables 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.
8
1,016 npm
1
MIT
Imagen MCP Server
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TDQS
A4.2/5.0
Scored across 6 tools
Disambiguation5/5
Each tool has a clearly distinct purpose with no ambiguity. generate_image creates single images, start_image_batch handles batch generation, get_next_image retrieves batch results, get_batch_status checks batch progress, list_models enumerates available models, and get_model_details provides specific model information. The tools cover different aspects of the image generation workflow without overlap.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern with snake_case throughout. The naming is predictable and readable: generate_image, start_image_batch, get_next_image, get_batch_status, list_models, and get_model_details. This consistency makes it easy for agents to understand the tool purposes from their names alone.
Tool Count5/5
Six tools is well-scoped for an image generation server. Each tool earns its place by covering distinct aspects of the workflow: single generation, batch generation, batch retrieval, status checking, model listing, and model details. This count provides comprehensive coverage without being overwhelming or too sparse for the domain.
Completeness5/5
The tool surface provides complete coverage for image generation workflows. It supports both single and batch generation, includes status monitoring and result retrieval for batches, and offers comprehensive model discovery and information tools. There are no obvious gaps—agents can generate images, track progress, retrieve results, and make informed model selections.
Maintenance
ActivityInactive
ResponsivenessNo issues