Skip to main content
Glama

gemini_image_set

Create a consistent set of images: generate a master image, then variations or scene-specific images that reference it.

Instructions

Generate a consistent SET of images: a master image from master_prompt, then one image per scene that references the master so the subject/style stays consistent. Provide scenes (explicit per-image prompts) OR count (variations of the master).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedNoSeed for reproducible generation; random if omitted
asyncNoRun in the background and return a job_id immediately instead of the image, so a long (Pro/4K) generation cannot hit the host tools/call timeout (-32001). Poll gemini_get_result with the job_id to fetch the result (jobs are per-process and expire ~10 min after completion).
countNoNumber of variations of master_prompt (when scenes omitted)
modelNoModel id override (default: server default; see gemini_list_models). gemini-3.1-flash-image (Nano Banana 2) is the versatile generalist workhorse — balances speed with state-of-the-art 4K generation, world knowledge, and reliable text rendering; excels at multi-reference-image processing and consistency. gemini-3-pro-image (Nano Banana Pro) is the premium choice for the most complex visual tasks — highest world knowledge, advanced localization, accurate brand consistency, precision creative control. gemini-3.1-flash-lite-image (Nano Banana 2 Lite) is the fastest/cheapest for simple tasks (1K only, no search grounding).
inlineNoReturn base64 images inline instead of writing to disk
scenesNoPer-image prompts (1-8); each references the master
confirmNoMust be true to proceed. Without this, the tool returns a preview.
basenameNoBase filename prefix for output images (default: slugified master_prompt)
image_sizeNoOutput resolution (512 = 0.5K, Flash-only)
output_dirNoDirectory to write images to (default: $GEMINI_OUTPUT_DIR or cwd)
timeout_msNoUpstream request timeout in ms for this call (default: $GEMINI_TIMEOUT_MS, else 60000 — or 120000 when image_size is 4K, which routinely runs past 60s)
aspect_ratioNoOutput aspect ratio
google_searchNoGround the image in live Google Search results (current events, weather, data)
master_imagesNoReference image paths passed to the master generation call
master_promptYesPrompt for the master/reference image
from_clipboardNoUse the image currently on the macOS system clipboard as an input (downscaled to JPEG)
reference_modeNomaster: every image references the master (default). chain: each references the previous.
thinking_levelNoReasoning depth (Gemini 3 models); higher can help complex/structural edits
idempotency_keyNoOpaque idempotency key: a repeat call with the same key returns the recorded result (reused: true) instead of billing a new generation. Set it when retrying after a host timeout (-32001) to avoid a duplicate charge.
master_images_urlNoReference images passed to the master AND to every scene call (fetched once) as public https URLs — the SERVER downloads them, so no image bytes travel through the conversation. Preferred over images_base64, which costs ~14k tokens per photo and breaks if a file read was truncated. Max 15MB each; must be a directly-linked image (Content-Type image/*).
master_images_base64NoReference images as base64 strings or data URIs for master generation. Last resort: prefer master_images_url or master_images_file_uris, which keep image bytes out of the conversation
master_images_file_urisNoReference images passed to the master AND to every scene call by Gemini Files API reference ("files/<id>", or the full uri) from gemini_upload_file or POST /upload. Upload once, then reference it across as many calls as you like — no bytes are re-sent and none enter the conversation. Files are retained ~48h, after which the reference stops resolving.
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Describes the generation flow (master then scenes) and options like async, inline, confirm. Adds context beyond annotations (readOnlyHint=false, openWorldHint=true) without contradicting them.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences efficiently convey core purpose and usage modes. No redundant information, front-loaded with key action.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers the main functionality and usage patterns for a complex tool with 22 parameters. Lacks return value details but implies images are returned inline or written to disk.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline 3. Description summarizes the two modes but adds minimal new parameter meaning, leaving details to the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool generates a consistent set of images using a master prompt and per-scene prompts or variations. Distinguishes from siblings like single image generation or editing.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly explains the two usage modes (scenes or count) implying when to use each. Does not explicitly exclude sibling tools but provides clear context for usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/chrischall/gemini-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server