Siray Image MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| generate_imageA | Generate an image with Siray and save it into the current project. Args: prompt: What to draw. Be specific about subject, style, lighting, background. out_dir: Absolute path of the folder to save into, e.g. the project's assets/images folder. Created if missing. model: Siray model id, e.g. "bytedance/seedream-4.5-t2i", "google/nano-banana-pro-t2i", "black-forest-labs/flux-1.1-pro-ultra-t2i". Omit for the default. Call list_models() for the live list. image: Optional reference image for image-to-image / editing — a URL or a local file path. When you pass this you MUST also pass an -i2i / -edit model (e.g. "google/nano-banana-pro-i2i"); -t2i models reject it. Note: bytedance/seedream-* ignores size and aspect ratio entirely. size: exact output dimensions, e.g. "1920x1080". Use this whenever the user names dimensions. openai/* models render at that size natively; for other models the nearest aspect ratio is requested and the file is resized to exactly this. aspect_ratio: e.g. "16:9", "1:1", "9:16". Ignore when passing size. seed: Integer for reproducible output. Returns the saved file path(s). |
| generate_backdropA | Generate a background plate that the project's UI can sit on top of. STOP: before calling this, read the project's actual colours — the CSS custom
properties, tailwind.config, theme file, or an existing screenshot — and pass
them as The prompt is rewritten to enforce background-plate discipline (no text, no
logos, no centre subject, calm negative space, tone set opposite the overlay
colour), then the saved file is measured locally: worst-region contrast against
The file is ALWAYS kept — it is already paid for. A FAILED verdict tells you exactly what to change if you choose to regenerate; it is not an automatic retry. Args: prompt: The scene or texture, e.g. "soft abstract mesh gradient". out_dir: Absolute path to save into. Created if missing. palette: Hex colours from the project, e.g. ["#0f1115", "#6ea8fe"]. A single comma-separated string works too. overlay_text_color: Hex colour of the text/UI going on top, e.g. "#e7e9ee". size: Exact dimensions, e.g. "1920x1080". model: Omit for the default. Do not use bytedance/seedream-* for a non-square backdrop — it only renders 2048x2048. tileable: True for a repeating pattern; the edge seam is then measured. seed: Integer for reproducible output. Returns the saved path, the measurements, and a PASS/FAILED verdict. |
| list_modelsA | List Siray's active image models, live from the API. Default is marked. Ids ending in -t2i are text-to-image; -i2i / -edit / -ref2i need an |
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 3 tools
The two generation tools are clearly separated by purpose: generate_image handles general image creation/editing, while generate_backdrop is a specialized, measurement-driven operation for background plates. list_models is entirely distinct, so an agent can confidently select the right tool by name and description.
All tool names follow the same verb_noun snake_case pattern: generate_image, generate_backdrop, and list_models. There are no mixed conventions, vague single-word names, or inconsistent prefixes.
Three tools is well-scoped for a narrow image-generation MCP: one general generation tool, one specialized backdrop tool, and one model discovery tool. Each tool earns its place, and the count is within the ideal range.
The server covers the core image-generation lifecycle: discovering available models, generating images from prompts, supporting image-to-image/editing via the image argument, and providing a dedicated backdrop workflow with built-in validation. There are no obvious dead ends or missing critical operations for an image generation domain.