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# opencode-cloudflare-image-mcp

An [MCP (Model Context Protocol)](https://modelcontextprotocol.io/) server that gives any MCP-capable AI model (Claude Desktop, opencode, etc.) the ability to **generate images** from text prompts and **describe images** in text, using [Cloudflare Workers AI](https://developers.cloudflare.com/workers-ai/) models.

## Features

- **generate_image** — Generate images from text prompts using Cloudflare text-to-image models (FLUX.1 Schnell, SDXL, DreamShaper, and more).
- **describe_image** — Generate text descriptions of images using Cloudflare image-to-text models (LLaVA, Moondream, uForm).
- **list_models** — Discover all supported models with aliases and defaults.
- Generated images are saved to disk; the file path is returned to the model.
- Image-to-text supports both local file paths and HTTP(S) URLs as input.

## Install via OpenCode Agent

Paste this to your opencode agent to install and configure:

```
Install the opencode-cloudflare-image-mcp MCP server globally, then add it to my opencode.json config at ~/.config/opencode/opencode.json as a local MCP server with:
- type: local
- command: ["node", "E:/Image-MCP/dist/index.js"] (or your install path)
- environment with CLOUDFLARE_ACCOUNT_ID and CLOUDFLARE_API_TOKEN
```

Or manually:

```bash
npm install -g opencode-cloudflare-image-mcp
```

Then add to `~/.config/opencode/opencode.json`:

```json
{
  "mcp": {
    "cloudflare-image": {
      "type": "local",
      "command": ["node", "E:/Image-MCP/dist/index.js"],
      "environment": {
        "CLOUDFLARE_ACCOUNT_ID": "your-account-id",
        "CLOUDFLARE_API_TOKEN": "your-api-token"
      },
      "enabled": true
    }
  }
}
```

> **Note:** Use `"environment"` not `"env"` for the env vars in opencode config.

## Install (Manual)

```bash
npm install -g opencode-cloudflare-image-mcp
```

Or use directly with `npx`:

```bash
npx opencode-cloudflare-image-mcp
```

## Configuration

### Environment Variables

| Variable | Required | Default | Purpose |
|----------|----------|---------|---------|
| `CLOUDFLARE_ACCOUNT_ID` | yes | — | Cloudflare account ID |
| `CLOUDFLARE_API_TOKEN` | yes | — | Bearer token for API auth |
| `IMAGE_MCP_OUTPUT_DIR` | no | `./generated-images` | Where generated PNG files are saved |
| `IMAGE_MCP_DEFAULT_T2I_MODEL` | no | `@cf/black-forest-labs/flux-1-schnell` | Default text-to-image model |
| `IMAGE_MCP_DEFAULT_I2T_MODEL` | no | `@cf/llava-hf/llava-1.5-7b-hf` | Default image-to-text model |

### Getting a Cloudflare API Token

1. Go to the [Cloudflare dashboard](https://dash.cloudflare.com/).
2. Navigate to **My Profile** → **API Tokens**.
3. Click **Create Token**.
4. Use the **Workers AI** template, or create a custom token with `Workers AI:Read` permission.
5. Copy the token and set it as `CLOUDFLARE_API_TOKEN`.

Your Cloudflare Account ID is visible on the dashboard overview page or any domain's overview page.

### Client Configuration

#### Claude Desktop

Add to `claude_desktop_config.json`:

```json
{
  "mcpServers": {
    "cloudflare-image": {
      "command": "npx",
      "args": ["-y", "opencode-cloudflare-image-mcp"],
      "env": {
        "CLOUDFLARE_ACCOUNT_ID": "your-account-id",
        "CLOUDFLARE_API_TOKEN": "your-api-token"
      }
    }
  }
}
```

#### opencode / Generic stdio

```json
{
  "mcp": {
    "cloudflare-image": {
      "type": "local",
      "command": ["npx", "-y", "opencode-cloudflare-image-mcp"],
      "env": {
        "CLOUDFLARE_ACCOUNT_ID": "your-account-id",
        "CLOUDFLARE_API_TOKEN": "your-api-token"
      }
    }
  }
}
```

## Usage

Once connected, the AI model can call these tools:

### generate_image

```
Generate an image of a cyberpunk lizard in a neon-lit city
```

The model will call `generate_image` with the prompt. The image is saved to disk and the file path is returned.

### describe_image

```
Describe the image at /path/to/image.png
```

Or with a URL:

```
What's in this image? https://example.com/photo.jpg
```

### list_models

```
What image models are available?
```

## Supported Models

### Text-to-Image

| Alias | Model ID | Default |
|-------|----------|---------|
| `flux-schnell` | `@cf/black-forest-labs/flux-1-schnell` | yes |
| `flux-2-klein-4b` | `@cf/black-forest-labs/flux-2-klein-4b` | |
| `sdxl-lightning` | `@cf/bytedance/stable-diffusion-xl-lightning` | |
| `dreamshaper` | `@cf/lykon/dreamshaper-8-lcm` | |
| `sdxl-base` | `@cf/stabilityai/stable-diffusion-xl-base-1.0` | |

### Image-to-Text

| Alias | Model ID | Default |
|-------|----------|---------|
| `llava` | `@cf/llava-hf/llava-1.5-7b-hf` | yes |
| `uform` | `@cf/unum/uform-gen2-qwen-500m` | |
| `moondream` | `@cf/moondream/moondream3.1-9B-A2B` | |

## Development

```bash
pnpm install
pnpm test
pnpm build
```

## License

MIT

TDQS

A3.8/5.0

Scored across 3 tools

Disambiguation5/5

Each tool has a distinct purpose: describe_image processes an input image, generate_image creates an image from text, and list_models provides model discovery. No overlap or ambiguity.

Naming Consistency5/5

All tool names follow a consistent snake_case verb_noun pattern (describe_image, generate_image, list_models), making them predictable and easy to understand.

Tool Count4/5

With only 3 tools, the server is focused but covers core image operations. While slightly thin, it's appropriate for a specialized image AI server and doesn't feel overcrowded.

Completeness4/5

The server provides generate and describe functionality along with model discovery. Minor gaps exist (e.g., no edit or delete endpoints), but the surface is adequate for basic image generation and description tasks.

Maintenance

ActivityStale
ResponsivenessNo issues