Replicate Flux MCP

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Replicate Flux MCP is an advanced Model Context Protocol (MCP) server that empowers AI assistants to generate high-quality images and vector graphics. By default it uses black-forest-labs/flux-schnell for raster images and recraft-ai/recraft-v3-svg for SVG output. You can override the curated image/SVG models through environment variables, and image tools also accept a per-call `model_id` override from the built-in allowlist.

📑 Table of Contents

• Getting Started & Integration
  • Setup Process
  • Cursor Integration
  • Claude Desktop Integration
  • Smithery Integration
• Glama.ai Integration
• Codex Integration
• Features
• Documentation
  • Available Tools
  • Available Resources
  • Available Prompts
  • Structured Output
  • Environment Variables
• Development
  • Testing
• Technical Details
• Troubleshooting
• Contributing
• License
• Resources
• Examples

🚀 Getting Started & Integration

Setup Process

1. Obtain a Replicate API Token

2. Choose Your Integration Method

3. Ask Your AI Assistant to Generate an Image

4. Explore Advanced Features

Cursor Integration

Method 1: Using mcp.json

1. Create or edit the `.cursor/mcp.json` file in your project directory:

{
  "mcpServers": {
    "replicate-flux-mcp": {
      "command": "env REPLICATE_API_TOKEN=YOUR_TOKEN npx",
      "args": ["-y", "replicate-flux-mcp"]
    }
  }
}

2. Replace `YOUR_TOKEN` with your actual Replicate API token

3. Restart Cursor to apply the changes

Method 2: Manual Mode

1. Open Cursor and go to Settings

2. Navigate to the "MCP" or "Model Context Protocol" section

3. Click "Add Server" or equivalent

4. Enter the following command in the appropriate field:

env REPLICATE_API_TOKEN=YOUR_TOKEN npx -y replicate-flux-mcp

5. Replace `YOUR_TOKEN` with your actual Replicate API token

6. Save the settings and restart Cursor if necessary

Claude Desktop Integration

1. Create or edit the `mcp.json` file in your configuration directory:

{
  "mcpServers": {
    "replicate-flux-mcp": {
      "command": "npx",
      "args": ["-y", "replicate-flux-mcp"],
      "env": {
        "REPLICATE_API_TOKEN": "YOUR TOKEN"
      }
    }
  }
}

2. Replace `YOUR_TOKEN` with your actual Replicate API token

3. Restart Claude Desktop to apply the changes

Smithery Integration

This MCP server is available as a hosted service on Smithery, allowing you to use it without setting up your own server.

1. Visit Smithery and create an account if you don't have one

2. Navigate to the Replicate Flux MCP server page

3. Click "Add to Workspace" to add the server to your Smithery workspace

4. Configure your MCP client (Cursor, Claude Desktop, etc.) to use your Smithery workspace URL

For more information on using Smithery with your MCP clients, visit the Smithery documentation.

Glama.ai Integration

This MCP server is also available as a hosted service on Glama.ai, providing another option to use it without local setup.

1. Visit Glama.ai and create an account if you don't have one

2. Go to the Replicate Flux MCP server page

3. Click "Install Server" to add the server to your workspace

4. Configure your MCP client to use your Glama.ai workspace

For more information, visit the Glama.ai MCP servers documentation.

Codex Integration

Add the server to `~/.codex/config.toml`:

[mcp_servers.replicate]
command = "npx"
args = ["-y", "replicate-flux-mcp"]
env = { REPLICATE_API_TOKEN = "your-replicate-api-token", REPLICATE_IMAGE_MODEL_ID = "your-image-model-id", REPLICATE_SVG_MODEL_ID = "your-svg-model-id" }
startup_timeout_sec = 30_000

Replace the env values as needed. If you omit `REPLICATE_IMAGE_MODEL_ID` / `REPLICATE_SVG_MODEL_ID`, the server uses `black-forest-labs/flux-schnell` for images and `recraft-ai/recraft-v3-svg` for SVGs.

Curated tools validate model overrides against built-in allowlists:

• Image generation: `black-forest-labs/flux-schnell`, `google/imagen-4`, `black-forest-labs/flux-kontext-pro`, `ideogram-ai/ideogram-v3-turbo`, `black-forest-labs/flux-1.1-pro`, `black-forest-labs/flux-dev`
• SVG generation: `recraft-ai/recraft-v3-svg`
• Legacy `run_model` extras: `minimax/video-01`, `luma/reframe-video`, `topazlabs/video-upscale`, `topazlabs/image-upscale`, `szcho/codeformer`, `tencentarc/gfpgan`

🌟 Features

• 🖼️ High-Quality Image Generation — Flux Schnell raster images by default, with environment-variable and per-tool image model overrides.
• 🎨 Vector Graphics — Recraft V3 SVG for logos, icons, and diagrams.
• 📊 Batch + Variants — Generate N images from N prompts or N variants of one prompt (seed-based or prompt-modifier-based).
• 🧩 Arbitrary Replicate Models — `run_replicate_model` escape hatch accepts any `owner/name[:version]` reference, with `get_model_schema` introspection for the OpenAPI input schema. Optional allowlist via `REPLICATE_MODEL_ALLOWLIST`.
• 📦 Structured Output — Every `generate_*` tool returns machine-readable `structuredContent` alongside human-readable content, matching a per-tool `outputSchema` (URL, prompt, format, aspect ratio, per-variant seed, etc).
• ⏳ Progress Notifications — Batch and variant generation emit `notifications/progress` for clients that opt in via `progressToken`, so long runs aren't black-boxed.
• 💬 Curated Prompts — 5 ready-made prompt templates (`logo`, `portrait`, `svg-icon`, `product-shot`, `isometric-diagram`) surfaced in Claude Desktop's slash palette and Cursor's `@`-menu.
• 🏷️ Proper Tool Annotations — `readOnlyHint` / `destructiveHint` / `openWorldHint` / `idempotentHint` set correctly so clients can reason about safety and cost.
• 🪵 Structured Logging — Server-side errors travel over `notifications/message` instead of stderr.
• 🔌 Universal MCP Compatibility — MCP protocol 2025-11-25; works with Claude Desktop, Cursor, Cline, Zed, and any spec-compliant client.
• 🔍 Generation History — Browse past runs through `imagelist`, `svglist`, and `predictionlist` resources.

📚 Documentation

Available Tools

`generate_image`

Generates an image based on a text prompt using the configured image model (allowlist only).

{
  prompt: string;                // Required: Text description of the image to generate
  model_id?: string;             // Optional: Override image model (allowlist only)
  seed?: number;                 // Optional: Random seed for reproducible generation
  go_fast?: boolean;             // Optional: Run faster predictions with optimized model (default: true)
  megapixels?: "1" | "0.25";     // Optional: Image resolution (default: "1")
  num_outputs?: number;          // Optional: Number of images to generate (1-4) (default: 1)
  aspect_ratio?: string;         // Optional: Aspect ratio (e.g., "16:9", "4:3") (default: "1:1")
  output_format?: string;        // Optional: Output format ("webp", "jpg", "png") (default: "webp")
  output_quality?: number;       // Optional: Image quality (0-100) (default: 80)
  num_inference_steps?: number;  // Optional: Number of denoising steps (1-4) (default: 4)
  disable_safety_checker?: boolean; // Optional: Disable safety filter (default: false)
  support_image_mcp_response_type?: boolean; // Optional: Return embedded image content when supported (default: true)
}

`generate_multiple_images`

Generates multiple images based on an array of prompts using the configured image model (allowlist only).

{
  prompts: string[];             // Required: Array of text descriptions for images to generate (1-10 prompts)
  model_id?: string;             // Optional: Override image model (allowlist only)
  seed?: number;                 // Optional: Random seed for reproducible generation
  go_fast?: boolean;             // Optional: Run faster predictions with optimized model (default: true)
  megapixels?: "1" | "0.25";     // Optional: Image resolution (default: "1")
  aspect_ratio?: string;         // Optional: Aspect ratio (e.g., "16:9", "4:3") (default: "1:1")
  output_format?: string;        // Optional: Output format ("webp", "jpg", "png") (default: "webp")
  output_quality?: number;       // Optional: Image quality (0-100) (default: 80)
  num_inference_steps?: number;  // Optional: Number of denoising steps (1-4) (default: 4)
  disable_safety_checker?: boolean; // Optional: Disable safety filter (default: false)
  support_image_mcp_response_type?: boolean; // Optional: Return embedded image content when supported (default: true)
}

`generate_image_variants`

Generates multiple variants of the same image from a single prompt using the configured image model (allowlist only).

{
  prompt: string;                // Required: Text description for the image to generate variants of
  model_id?: string;             // Optional: Override image model (allowlist only)
  num_variants: number;          // Required: Number of image variants to generate (2-10, default: 4)
  prompt_variations?: string[];  // Optional: List of prompt modifiers to apply to variants (e.g., ["in watercolor style", "in oil painting style"])
  variation_mode?: "append" | "replace"; // Optional: How to apply variations - 'append' adds to base prompt, 'replace' uses variations directly (default: "append")
  seed?: number;                 // Optional: Base random seed. Each variant will use seed+variant_index
  go_fast?: boolean;             // Optional: Run faster predictions with optimized model (default: true)
  megapixels?: "1" | "0.25";     // Optional: Image resolution (default: "1")
  aspect_ratio?: string;         // Optional: Aspect ratio (e.g., "16:9", "4:3") (default: "1:1")
  output_format?: string;        // Optional: Output format ("webp", "jpg", "png") (default: "webp")
  output_quality?: number;       // Optional: Image quality (0-100) (default: 80)
  num_inference_steps?: number;  // Optional: Number of denoising steps (1-4) (default: 4)
  disable_safety_checker?: boolean; // Optional: Disable safety filter (default: false)
  support_image_mcp_response_type?: boolean; // Optional: Return embedded image content when supported (default: true)
}

`generate_svg`

Generates SVG/vector output based on a text prompt using the configured SVG model (allowlist only).

{
  prompt: string;                // Required: Text description of the SVG to generate
  size?: string;                 // Optional: Size of the generated SVG (default: "1024x1024")
  style?: string;                // Optional: Style of the generated image (default: "any")
                                // Options: "any", "engraving", "line_art", "line_circuit", "linocut"
}

`prediction_list`

Retrieves a list of your recent predictions from Replicate.

{
  limit?: number;  // Optional: Maximum number of predictions to return (1-100) (default: 50)
}

`get_prediction`

Gets detailed information about a specific prediction.

{
  predictionId: string;  // Required: ID of the prediction to retrieve
}

`run_model`

Runs a whitelisted Replicate model with a raw input payload (useful for video or restoration models).

{
  model_id: string;                // Required: Replicate model id (allowlist only)
  input?: Record; // Optional: Raw input payload for the model
}

`run_replicate_model`

Runs any model hosted on Replicate by its `owner/name[:version]` reference. Use this as an escape hatch when none of the curated tools fit. Call `get_model_schema` first if you don't know the input shape.

{
  model: string;                              // Required: 'owner/name' or 'owner/name:version'
  input: Record;             // Required: Model input parameters
  prefer_wait?: number;                       // Optional: Seconds to block waiting for sync output (1-60, default 60)
  return_as?: "url" | "base64" | "both";      // Optional: How to return file outputs (default "url")
}

Set the `REPLICATE_MODEL_ALLOWLIST` env var (comma-separated `owner/name` entries) to restrict which models can be invoked. Unset = any model allowed. Set-but-empty = deny all (the server fails closed rather than silently allowing everything).

`get_model_schema`

Fetches the OpenAPI input schema and description for a Replicate model so you can pass the right parameters to `run_replicate_model`.

{
  model: string;  // Required: Replicate model reference in 'owner/name' form
}

Available Resources

`imagelist`

Browse your history of generated images created with the configured image model.

`svglist`

Browse your history of generated SVG outputs created with the configured SVG model.

`predictionlist`

Browse all your Replicate predictions history.

Available Prompts

Curated templates surfaced in Claude Desktop's slash menu and Cursor's `@`-palette. Each one fills in sensible defaults then delegates to the relevant generation tool.

Structured Output

Every `generate_*` tool returns both human-readable `content` (text + image blocks) and machine-readable `structuredContent` that matches the tool's `outputSchema`.

Clients that understand MCP structured output can consume URLs and metadata directly without parsing prose.

Environment Variables

💻 Development

1. Clone the repository:

git clone https://github.com/awkoy/replicate-flux-mcp.git
cd replicate-flux-mcp

2. Install dependencies:

npm install

3. Start the TypeScript watcher:

npm run watch

4. Build the project:

npm run build

5. Smoke-test the server with the MCP Inspector:

npm run inspector

6. Connect to Client:

{
  "mcpServers": {
    "image-generation-mcp": {
      "command": "npx",
      "args": [
        "/Users/{USERNAME}/{PATH_TO}/replicate-flux-mcp/build/index.js"
      ],
      "env": {
        "REPLICATE_API_TOKEN": "YOUR REPLICATE API TOKEN"
      }
    }
  }
}

Testing

This project currently has no automated test suite. Verification is done via:

• `npm run build` — TypeScript type-checking catches most regressions.
• `npm run inspector` — drives the built binary through the official MCP Inspector for end-to-end smoke testing of tools, resources, and prompts.

Contributions adding a proper test framework (e.g. Vitest + an MCP stdio client harness) are welcome.

⚙️ Technical Details

Stack

• Model Context Protocol SDK - Core MCP functionality for tool and resource management
• Replicate API - Provides access to state-of-the-art AI image generation models
• TypeScript - Ensures type safety and leverages modern JavaScript features
• Zod - Implements runtime type validation for robust API interactions

Configuration

The server can be configured by modifying the `CONFIG` object in `src/config/index.ts` or by setting the `REPLICATE_IMAGE_MODEL_ID` / `REPLICATE_SVG_MODEL_ID` environment variables to override the defaults:

export const CONFIG = {
  serverName: "replicate-flux-mcp",
  serverVersion: "0.4.0",
  imageModelId: process.env.REPLICATE_IMAGE_MODEL_ID ?? "black-forest-labs/flux-schnell",
  svgModelId: process.env.REPLICATE_SVG_MODEL_ID ?? "recraft-ai/recraft-v3-svg",
  pollingAttempts: 25,
  pollingInterval: 2000, // ms
  modelAllowlistConfigured: process.env.REPLICATE_MODEL_ALLOWLIST !== undefined,
  modelAllowlist: (process.env.REPLICATE_MODEL_ALLOWLIST ?? "")
    .split(",")
    .map((s) => s.trim())
    .filter(Boolean),
};

Switching models (no code changes)

Use env vars when launching the server (works with `npx`, Cursor, Claude Desktop, etc.). Curated image/SVG tool overrides must be in the built-in allowlists:

# Stay on defaults:
REPLICATE_API_TOKEN=YOUR_TOKEN npx -y replicate-flux-mcp

# Switch to other allowlisted models
REPLICATE_IMAGE_MODEL_ID="google/imagen-4" \
REPLICATE_SVG_MODEL_ID="recraft-ai/recraft-v3-svg" \
REPLICATE_API_TOKEN=YOUR_TOKEN \
npx -y replicate-flux-mcp

`modelAllowlist` is evaluated once at process start from `REPLICATE_MODEL_ALLOWLIST`. Restart the server after changing it.

🔍 Troubleshooting

Common Issues

Authentication Error

• Ensure your `REPLICATE_API_TOKEN` is correctly set in the environment
• Verify your token is valid by testing it with the Replicate API directly

Safety Filter Triggered

• The model has a built-in safety filter that may block certain prompts
• Try modifying your prompt to avoid potentially problematic content

Timeout Error

• For larger images or busy servers, you might need to increase `pollingAttempts` or `pollingInterval` in the configuration
• Default settings should work for most use cases

🤝 Contributing

Contributions are welcome! Please follow these steps to contribute:

1. Fork the repository

2. Create your feature branch (`git checkout -b feature/amazing-feature`)

3. Commit your changes (`git commit -m 'Add some amazing feature'`)

4. Push to the branch (`git push origin feature/amazing-feature`)

5. Open a Pull Request

For feature requests or bug reports, please create a GitHub issue. If you like this project, consider starring the repository!

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🔗 Resources

• Model Context Protocol Documentation
• Replicate API Documentation
• Try for Free Collection
• Flux Schnell Model
• Recraft V3 SVG Model
• MCP TypeScript SDK
• Smithery Documentation
• Glama.ai MCP Servers

🎨 Examples

Here are some examples of how to use the tools:

Batch Image Generation with `generate_multiple_images`

Create multiple distinct images at once with different prompts:

{
  "prompts": [
    "A red sports car on a mountain road", 
    "A blue sports car on a beach", 
    "A vintage sports car in a city street"
  ]
}

Image Variants with `generate_image_variants`

Create different interpretations of the same concept using seeds:

{
  "prompt": "A futuristic city skyline at night",
  "num_variants": 4,
  "seed": 42
}

Or explore style variations with prompt modifiers:

{
  "prompt": "A character portrait",
  "prompt_variations": [
    "in anime style", 
    "in watercolor style", 
    "in oil painting style", 
    "as a 3D render"
  ]
}

Made with ❤️ by Yaroslav Boiko