ai-image-generate/dist/src/ai-image-generate.js

MCP Server for image generation using Replicate's flux-schnell model

#!/usr/bin/env node
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";
import { ImageGenerationService } from '../src/imageService.js';
import dotenv from 'dotenv';
dotenv.config();
const model = process.env.REPLICATE_MODEL || 'black-forest-labs/flux-schnell';
const apiToken = process.env.REPLICATE_API_TOKEN;
if (!apiToken) {
    console.error('REPLICATE_API_TOKEN environment variable is required');
    process.exit(1);
}
const imageService = new ImageGenerationService(apiToken, model);
const server = new McpServer({
    name: "image-generator",
    version: "1.0.0"
});
server.tool("generate-image", "Generate an image based on a prompt", {
    prompt: z.string(),
    output_dir: z.string().describe("The absolute and full path to the directory where generated images will be saved."),
    filename: z.string().optional().describe("Base filename to save the image(s) with"),
    go_fast: z.boolean().optional(),
    megapixels: z.enum(["1", "2", "4"]).optional().describe("Resolution quality. Accepted values are \"1\", \"2\", or \"4\"."),
    num_outputs: z.number().min(1).max(4).optional().describe("Number of images to generate (1-4)."),
    aspect_ratio: z.enum(["1:1", "4:3", "16:9"]).optional().describe("Aspect ratio. Accepted values are \"1:1\", \"4:3\", or \"16:9\"."),
    output_format: z.enum(["webp", "png", "jpeg"]).optional().describe("Image format. Accepted values are \"webp\", \"png\", or \"jpeg\"."),
    output_quality: z.number().min(1).max(100).optional().describe("Compression quality (1-100)."),
    num_inference_steps: z.number().min(4).max(20).optional().describe("Number of denoising steps (4-20).")
}, async (params) => {
    try {
        const result = await imageService.generateImages(params);
        return {
            content: [{
                    type: "text",
                    text: JSON.stringify(result, null, 2)
                }]
        };
    }
    catch (error) {
        return {
            content: [{
                    type: "text",
                    text: JSON.stringify({
                        error: {
                            code: error.code || 'UNKNOWN_ERROR',
                            message: error.message,
                            details: error.details
                        }
                    }, null, 2)
                }]
        };
    }
});
async function main() {
    const transport = new StdioServerTransport();
    await server.connect(transport);
    console.error("Image Generation MCP Server running on stdio");
}
main().catch(async (error) => {
    const fs = require('fs').promises;
    await fs.appendFile('server.log', `${new Date().toISOString()} - ${error.stack || error}
`);
    console.error(error);
    process.exit(1);
});