Skip to main content
Glama
mcaimi

sd-api-mcp

by mcaimi
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

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

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
generate_imageA

Generate an image using Stable Diffusion. Waits for completion and returns the result.

Args:
    pipeline: Pipeline to use — "sd15", "sdxl", or "illustrious"
    positive_prompt: Text prompt describing the desired image
    model_checkpoint: Model checkpoint filename to use
    negative_prompt: Negative prompt (things to avoid)
    width: Image width in pixels (256-2048)
    height: Image height in pixels (256-2048)
    steps: Number of inference steps (1-150)
    cfg_scale: Classifier-free guidance scale (1-30)
    seed: Random seed (-1 for random)
    scheduler: Noise scheduler name
    loras: Optional list of LoRA configs, each with "name" (str) and optional "strength" (float 0-1)
    custom_vae: Optional custom VAE checkpoint filename
inpaint_imageA

Inpaint an image using Stable Diffusion. Waits for completion and returns the result.

Args:
    pipeline: Pipeline to use — "sd15", "sdxl", or "illustrious"
    positive_prompt: Text prompt describing what to paint in the masked region
    model_checkpoint: Model checkpoint filename to use
    image_base64: Base64-encoded input image
    mask_base64: Base64-encoded mask image (white = inpaint region)
    negative_prompt: Negative prompt (things to avoid)
    steps: Number of inference steps (1-150)
    cfg_scale: Classifier-free guidance scale (1-30)
    seed: Random seed (-1 for random)
    scheduler: Noise scheduler name
    loras: Optional list of LoRA configs, each with "name" (str) and optional "strength" (float 0-1)
    custom_vae: Optional custom VAE checkpoint filename
submit_generateA

Submit an image generation job without waiting. Returns the job ID for manual polling.

Args:
    pipeline: Pipeline to use — "sd15", "sdxl", or "illustrious"
    positive_prompt: Text prompt describing the desired image
    model_checkpoint: Model checkpoint filename to use
    negative_prompt: Negative prompt (things to avoid)
    width: Image width in pixels (256-2048)
    height: Image height in pixels (256-2048)
    steps: Number of inference steps (1-150)
    cfg_scale: Classifier-free guidance scale (1-30)
    seed: Random seed (-1 for random)
    scheduler: Noise scheduler name
    loras: Optional list of LoRA configs, each with "name" (str) and optional "strength" (float 0-1)
    custom_vae: Optional custom VAE checkpoint filename
submit_inpaintA

Submit an inpainting job without waiting. Returns the job ID for manual polling.

Args:
    pipeline: Pipeline to use — "sd15", "sdxl", or "illustrious"
    positive_prompt: Text prompt describing what to paint in the masked region
    model_checkpoint: Model checkpoint filename to use
    image_base64: Base64-encoded input image
    mask_base64: Base64-encoded mask image (white = inpaint region)
    negative_prompt: Negative prompt (things to avoid)
    steps: Number of inference steps (1-150)
    cfg_scale: Classifier-free guidance scale (1-30)
    seed: Random seed (-1 for random)
    scheduler: Noise scheduler name
    loras: Optional list of LoRA configs, each with "name" (str) and optional "strength" (float 0-1)
    custom_vae: Optional custom VAE checkpoint filename
batch_generateA

Submit a batch of image generation requests (up to 10).

Args:
    pipeline: Pipeline to use — "sd15", "sdxl", or "illustrious"
    requests: List of generation request objects, each with at least "positive_prompt" and "model_checkpoint"
compare_modelsA

Generate images from multiple models using the same prompt for comparison.

Args:
    pipeline: Pipeline to use — "sd15", "sdxl", or "illustrious"
    positive_prompt: Text prompt for all models
    models: List of model checkpoint filenames to compare (2-6)
    negative_prompt: Negative prompt (things to avoid)
    width: Image width in pixels (256-2048)
    height: Image height in pixels (256-2048)
    steps: Number of inference steps (1-150)
    cfg_scale: Classifier-free guidance scale (1-30)
    seed: Fixed seed for fair comparison (default 42)
    scheduler: Noise scheduler name
list_modelsB

List available models.

Args:
    model_type: Model architecture — "sd15" or "sdxl"
    resource_type: Resource category — "checkpoints", "loras", or "vaes"
get_model_metadataA

Get metadata for a specific model from its safetensors header.

Args:
    model_type: Model architecture — "sd15" or "sdxl"
    resource_type: Resource category — "checkpoints", "loras", or "vaes"
    model_name: Model filename
list_jobsA

List all jobs with their current status, progress, and timing information.

get_job_statusB

Get the current status and result of a specific job.

Args:
    job_id: The job identifier returned when the job was submitted
cancel_jobA

Cancel a pending or running job.

Args:
    job_id: The job identifier to cancel
health_checkA

Check if the Stable Diffusion API is healthy and reachable.

system_infoA

Get system information including GPU device, dtype, model cache stats, and job queue stats.

list_schedulersA

List all available noise schedulers that can be used for image generation.

get_app_settingsA

Get the current application configuration parameters.

merge_modelsA

Merge two model checkpoints. Submits the job and waits for completion.

Args:
    model_type: Model architecture — "sd15" or "sdxl"
    base_model: Base model checkpoint filename
    target_model: Target model checkpoint filename
    output_name: Output checkpoint filename for the merged model
    method: Merge method — "linear", "slerp", "additive", or "subtract"
    alpha: Merge strength (0.0-1.0)
    preserve_metadata: Whether to preserve model metadata
batch_merge_modelsA

Merge a base model with multiple target models in batch. Waits for completion.

Args:
    model_type: Model architecture — "sd15" or "sdxl"
    base_model: Base model checkpoint filename
    target_models: List of target model checkpoint filenames
    method: Merge method — "linear", "slerp", "additive", or "subtract"
    alpha: Merge strength (0.0-1.0)
    output_subdir: Output subdirectory name for merged models
    preserve_metadata: Whether to preserve model metadata
recipe_mergeB

Execute a multi-step merge recipe. Each step merges the current result with a new target. Waits for completion.

Args:
    model_type: Model architecture — "sd15" or "sdxl"
    base_model: Base model checkpoint filename
    steps: List of merge steps, each with "target_model" (str), "method" (str), and "alpha" (float 0-1)
    output_name: Output checkpoint filename for the final merged model

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

A3.7/5.0

Scored across 18 tools

Disambiguation4/5

Most tools target distinct operations (sync generation, async submission, batch, compare, merge variants), and descriptions clarify the differences. A few near pairs such as generate_image/submit_generate and batch_merge_models/recipe_merge could cause misselection, but the descriptions separate them well.

Naming Consistency3/5

The dominant verb_noun pattern (list_models, get_job_status, merge_models) is readable, but there are notable deviations like health_check, system_info, recipe_merge, and submit_generate. The mix of verb-first and noun-first names is inconsistent enough to slow an agent down.

Tool Count4/5

Eighteen tools is slightly above the ideal 3-15 range, but the breadth is justified by covering generation, async jobs, model metadata, system health, and model merging. It feels like a complete API surface rather than an inflated one.

Completeness4/5

The surface covers generation, inpainting, async job lifecycle, model listing/metadata, schedulers, and multiple merge workflows. Obvious gaps are minor, such as the lack of a general image-to-image tool or batch async variants, but core workflows have no dead ends.

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources