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sd-api-mcp

by mcaimi
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Python
Hybrid

sd-api-mcp

An MCP (Model Context Protocol) server that exposes a Stable Diffusion REST API to AI agents. Supports SD1.5, SDXL, and Illustrious XL pipelines for text-to-image generation, inpainting, model management, and model merging.

It sits on top of the SD API Backend project.

Built with the MCP Python SDK and managed with uv.

Requirements

Related MCP server: Segmind MCP Server

Installation

git clone <repo-url>
cd sd-api-mcp
uv sync

Usage

stdio (default)

For use with MCP clients that manage the server process (e.g., Claude Code, Claude Desktop):

uv run sd-api-mcp

SSE

uv run sd-api-mcp --transport sse --host 0.0.0.0 --port 8080

Streamable HTTP (recommended for networked deployments)

uv run sd-api-mcp --transport streamable-http --host 0.0.0.0 --port 8080

The MCP endpoint will be available at http://<host>:<port>/mcp.

Claude Desktop configuration

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "stable-diffusion": {
      "command": "uv",
      "args": ["run", "--project", "/path/to/sd-api-mcp", "sd-api-mcp"],
      "env": {
        "SD_API_URL": "http://localhost:8000"
      }
    }
  }
}

Claude Code configuration

claude mcp add stable-diffusion -- uv run --project /path/to/sd-api-mcp sd-api-mcp

OpenCode configuration

Add to your opencode.json:

{
  "mcp": {
    "stable-diffusion": {
      "type": "stdio",
      "command": "uv",
      "args": ["run", "--project", "/path/to/sd-api-mcp", "sd-api-mcp"],
      "env": {
        "SD_API_URL": "http://localhost:8000"
      }
    }
  }
}

For SSE or Streamable HTTP transports, start the server separately and use a remote URL instead:

{
  "mcp": {
    "stable-diffusion": {
      "type": "sse",
      "url": "http://localhost:8080/sse"
    }
  }
}

Environment Variables

Variable

Default

Description

SD_API_URL

http://localhost:8000

Base URL of the Stable Diffusion API

SD_POLL_INTERVAL

2.0

Seconds between job status polls

SD_POLL_TIMEOUT

600.0

Maximum seconds to wait for job completion

MCP_TRANSPORT

stdio

Transport protocol (stdio, sse, streamable-http)

MCP_HOST

127.0.0.1

Bind address for SSE/HTTP transports

MCP_PORT

8080

Port for SSE/HTTP transports

Available Tools

Image Generation

Tool

Description

generate_image

Generate an image and wait for the result

inpaint_image

Inpaint a masked region and wait for the result

submit_generate

Submit a generation job, return the job ID immediately

submit_inpaint

Submit an inpainting job, return the job ID immediately

batch_generate

Submit up to 10 generation requests at once

compare_models

Generate with 2-6 models using the same prompt for comparison

All generation tools accept a pipeline parameter: "sd15", "sdxl", or "illustrious".

Model Management

Tool

Description

list_models

List available checkpoints, LoRAs, or VAEs

get_model_metadata

Read metadata from a model's safetensors header

Job Management

Tool

Description

list_jobs

List all jobs with status and progress

get_job_status

Get status and result of a specific job

cancel_job

Cancel a pending or running job

System

Tool

Description

health_check

Check if the SD API is reachable

system_info

Get GPU, cache, and queue statistics

list_schedulers

List available noise schedulers

get_app_settings

Get current configuration parameters

Model Merging

Tool

Description

merge_models

Merge two checkpoints (linear, slerp, additive, subtract)

batch_merge_models

Merge a base model with multiple targets

recipe_merge

Execute a multi-step merge recipe

Examples

Generate an image (agent perspective)

An AI agent would call the generate_image tool with:

{
  "pipeline": "sdxl",
  "positive_prompt": "a cat sitting on a windowsill, golden hour lighting, photorealistic",
  "negative_prompt": "blurry, low quality",
  "model_checkpoint": "dreamshaperXL_v2.safetensors",
  "width": 1024,
  "height": 1024,
  "steps": 30,
  "cfg_scale": 7.0,
  "seed": -1,
  "scheduler": "DPM++ 2M"
}

The tool submits the job to the SD API, polls until completion, and returns the full result including the generated image.

List available models

{
  "model_type": "sdxl",
  "resource_type": "checkpoints"
}

Merge two models

{
  "model_type": "sd15",
  "base_model": "v1-5-pruned.safetensors",
  "target_model": "dreamshaper_8.safetensors",
  "output_name": "merged_model.safetensors",
  "method": "slerp",
  "alpha": 0.5
}

Project Structure

src/sd_api_mcp/
    __init__.py   # CLI entry point with transport selection
    server.py     # MCPServer instance and tool definitions
    client.py     # Async HTTP client for the SD API

License

This project is licensed under the GNU General Public License v3.0. See LICENSE for details.

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