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jashwanth0712

RunPod Image MCP Server

generate_image

Generate images from detailed text descriptions using Seedream V4 T2I, supporting photorealistic and artistic styles.

Instructions

Generate images from text descriptions using Seedream V4 T2I.

This tool creates high-quality photorealistic or artistic images from text prompts.
Jobs are processed asynchronously and typically complete in 30-90 seconds.

Args:
    prompt: Detailed text description of the desired image. Be specific about
        style, composition, lighting, colors, and subject matter.
    negative_prompt: Elements to exclude from the image (e.g., "blurry, low quality,
        distorted faces"). Optional but recommended for better results.
    size: Image dimensions in format "width*height" (e.g., "2048*2048").
        Valid range: 1024-4096 pixels for both width and height.
        Default: "2048*2048"
    seed: Random seed for reproducibility. Use -1 for random generation (default),
        or provide a specific number to reproduce results.
    enable_safety_checker: Enable content safety filtering. Default: true.
        Set to false only if you need to bypass content filtering.
    max_wait_seconds: Maximum time to wait for job completion in seconds.
        Default: 300 (5 minutes). Increase for very large images.

Returns:
    Success message with image URL, generation details, and job ID for status tracking.
    Format: "✓ Image generated successfully!

URL: ... Size: ... Seed: ... Job ID: ..."

Examples:
    - "A photorealistic sunset over snow-capped mountains with dramatic clouds"
    - "An oil painting of a medieval castle on a cliff, fantasy art style"
    - "Product photo of a sleek smartphone on a white background, studio lighting"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYes
negative_promptNo
sizeNo2048*2048
seedNo
enable_safety_checkerNo
max_wait_secondsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description discloses key behaviors: async processing, safety checker feature, seed reproducibility, and return format. It could mention failure modes or rate limits, but current coverage is good.

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?

The description is well-structured with a clear intro, organized Args section, Returns format, and examples. Each sentence adds value, no redundancy.

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

Completeness5/5

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

Despite no annotations, the description covers purpose, all parameters with semantics, behavioral traits (async, safety), return format, and examples. It provides sufficient context for an agent to use the tool correctly.

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

Parameters5/5

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

Schema description coverage is 0%, and the description provides detailed semantics for all 6 parameters, including valid ranges, defaults, and usage tips (e.g., negative_prompt recommended, seed for reproducibility). This adds high value beyond the bare 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?

The description clearly states the tool creates images from text descriptions using Seedream V4 T2I. It specifies high-quality photorealistic or artistic output, and distinguishes from siblings by focusing on generation vs. status checking 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?

The description provides context on async processing and typical completion time, but does not explicitly state when to avoid using this tool or compare directly with sibling tools like edit_image. Examples help suggest appropriate use.

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

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