gemini-image-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| LOG_LEVEL | No | Log level (debug, info, or error) | info |
| OUTPUT_DIR | No | Default directory for saved images | ~/gemini-images |
| DEFAULT_MODEL | No | Default Gemini model | gemini-2.5-flash-image |
| GEMINI_API_KEY | Yes | Your Gemini API key | |
| MAX_COST_PER_HOUR | No | Max estimated cost (USD) per rolling hour (0 = unlimited) | 0 |
| REQUEST_TIMEOUT_MS | No | API request timeout in milliseconds | 60000 |
| SESSION_TIMEOUT_MS | No | Multi-turn session expiry in milliseconds | 1800000 |
| MAX_REQUESTS_PER_HOUR | No | Max image generations per rolling hour (0 = unlimited) | 0 |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| generate_imageA | Generate or edit images using Google Gemini. Provide just a prompt for text-to-image generation. Add image file paths to edit or use reference images. Set removeBackground to get a transparent PNG cutout in one call (local AI matte; works on any subject, no extra API cost). Returns the saved file path, model used, token counts, and estimated cost. |
| process_imageA | Process an existing image locally using sharp. Crop, resize, remove background, convert format, or trim whitespace. Free, fast, no API calls. For AI-powered editing (style changes, complex background removal), use generate_image with the image as input instead. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 2 tools
The two tools have completely distinct purposes: generate_image handles AI-powered generation/editing via Google Gemini, while process_image performs local image operations using sharp. Descriptions explicitly clarify when to use each, leaving no ambiguity.
Both tool names follow a consistent 'verb_noun' pattern: 'generate_image' and 'process_image'. This pattern is uniform and predictable, making it easy for an agent to infer functionality.
With only 2 tools, the server is minimalist but well-scoped for its purpose of image generation and processing. While more tools could be added (e.g., for metadata extraction), the current count is reasonable for a focused MCP server.
The tools cover the core image lifecycle: AI generation/editing and local processing (crop, resize, background removal, format conversion). Minor gaps exist (e.g., no direct download or metadata retrieval), but the essential workflows are supported.