概述

• 文生图模型为使用 Cloudflare Worker AI 部署 Flux 模型，是参照视频https://www.bilibili.com/video/BV1UbkcYcE24/?spm_id_from=333.337.search-card.all.click&vd_source=9ca2da6b1848bc903db417c336f9cb6b的复现
• Cursor MCP Server实现是参照文章https://juejin.cn/post/7485267450880229402#heading-9实现

Cloudflare部署 Flux 模型

获取Cloudflare账号和token

• 注册、登录等步骤省略

管理账号——账户API令牌——Workers AI——使用模版

继续以显示摘要

创建令牌

找到文生图模型github地址

Workers AI——模型——flux-1-schnell——了解更多

Guides——Tutorials——How to Build an Image Generator using Workers AI

https://developers.cloudflare.com/workers-ai/guides/tutorials/image-generation-playground/image-generator-flux/

部署文生图模型

github地址

https://github.com/kristianfreeman/workers-ai-image-playground?tab=readme-ov-file#readme

执行顺序：

1. git clone到本地
2. 修改配置文件

• 将.dev.vars.example改为.dev.vars
• 替换CLOUDFLARE_ACCOUNT_ID（账号）和CLOUDFLARE_API_TOKEN（令牌）

3. 执行npm install
4. 执行npm run preview（生产为preview）


5. 打开网页（http://localhost:8788），选择flux-1-schnell

输入prompt进行测试

Cursor调用MCP Server

实现一个调用Cloudflare Workers AI模型的MCP Server

参照文章（https://juejin.cn/post/7485267450880229402#heading-9）进行项目设置

项目设置

让我们从创建项目和安装依赖开始：

mkdir mcp-image-generator
cd mcp-image-generator
npm init -y
npm install @modelcontextprotocol/sdk zod dotenv
npm install --save-dev typescript @types/node

接下来，创建一个基本的TypeScript配置文件。在项目根目录创建tsconfig.json：

{"compilerOptions": {"target": "ES2020","module": "NodeNext","moduleResolution": "NodeNext","esModuleInterop": true,"outDir": "./dist","strict": true},"include": ["src/**/*"]
}

然后，创建一个.env文件来存储你的Cloudflare凭证：
ini 体验AI代码助手 代码解读复制代码CLOUDFLARE_ACCOUNT_ID=你的账户ID
CLOUDFLARE_API_TOKEN=你的API令牌

别忘了将这个文件添加到.gitignore，保护你的API密钥不被意外公开。

构建MCP服务器

直接替换src/index.ts文件

import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";
import fs from 'fs';
import path from 'path';
import os from 'os';
import * as dotenv from 'dotenv';// 加载环境变量
dotenv.config();// 创建MCP服务器
const server = new McpServer({name: "AI图片生成助手",version: "1.0.0"
});// 添加一个文生图工具
server.tool("generate-image-from-text","使用Cloudflare的Flux模型生成图像",{prompt: z.string().min(1, "提示文本不能为空").max(2048, "提示文本不能超过2048个字符").describe("用于生成图像的文本描述"),steps: z.number().int("步数必须是整数").max(8, "步数最大为8").default(4).describe("扩散步数，值越高质量越好但耗时更长"),outputPath: z.string().min(1, "输出路径不能为空").describe("生成图片的保存目录路径"),filename: z.string().min(1, "文件名不能为空").describe("保存的图片文件名，不需要包含扩展名")},async ({ prompt, steps = 4, outputPath, filename }) => {const CLOUDFLARE_ACCOUNT_ID = process.env.CLOUDFLARE_ACCOUNT_ID;const CLOUDFLARE_API_TOKEN = process.env.CLOUDFLARE_API_TOKEN;const url = `https://api.cloudflare.com/client/v4/accounts/${CLOUDFLARE_ACCOUNT_ID}/ai/run/@cf/black-forest-labs/flux-1-schnell`;console.log(url);try {// 调用Cloudflare APIconst response = await fetch(url, {method: 'POST',headers: {'Authorization': `Bearer ${CLOUDFLARE_API_TOKEN}`,'Content-Type': 'application/json'},body: JSON.stringify({prompt: prompt})});// 解析响应const responseData = await response.json() as { image?: string;[key: string]: unknown };if (!response.ok) {return {content: [{ type: "text", text: `调用API失败: ${response.status} ${response.statusText}` }]};}// 提取图像数据let imageBase64 = null;if (responseData.image) {imageBase64 = responseData.image as string;} else if (responseData.result && typeof responseData.result === 'object') {const resultObj = responseData.result as Record<string, unknown>;if (resultObj.image) {imageBase64 = resultObj.image as string;} else if (resultObj.data) {imageBase64 = resultObj.data as string;}}if (!imageBase64) {return {content: [{ type: "text", text: "API返回的数据中没有图像" }]};}// 图像处理逻辑将在下一步添加// 保存图像文件let targetFilePath = path.join(outputPath, `${filename}.jpg`);let actualSavePath = targetFilePath;let message = '';try {// 确保输出目录存在if (!fs.existsSync(outputPath)) {fs.mkdirSync(outputPath, { recursive: true });}// 测试目录是否可写const testFileName = path.join(outputPath, '.write-test');fs.writeFileSync(testFileName, '');fs.unlinkSync(testFileName);// 将Base64图像保存为文件const imageBuffer = Buffer.from(imageBase64, 'base64');fs.writeFileSync(targetFilePath, imageBuffer);message = `图像已成功生成并保存到: ${targetFilePath}`;} catch (fileError) {// 备用方案：保存到临时目录const tempDir = path.join(os.tmpdir(), 'mcp_generated_images');if (!fs.existsSync(tempDir)) {fs.mkdirSync(tempDir, { recursive: true });}actualSavePath = path.join(tempDir, `${filename}.jpg`);const imageBuffer = Buffer.from(imageBase64, 'base64');fs.writeFileSync(actualSavePath, imageBuffer);message = `由于权限问题无法保存到 ${targetFilePath}，已保存到临时位置: ${actualSavePath}`;}return {content: [{ type: "text", text: message }]};} catch (error: unknown) {const errorMessage = error instanceof Error ? error.message : String(error);return {content: [{ type: "text", text: `发生错误: ${errorMessage}` }]};}
}
);// 启动服务器
const transport = new StdioServerTransport();
await server.connect(transport);

编译和运行

在package.json中添加以下脚本：

"scripts": {"build": "tsc","start": "node dist/index.js"
}

然后编译并运行你的服务器：

npm run build

在Cursor中配置MCP服务

{"mcpServers": {"imageGenerator": {"command": "node","args": ["/Users/admin/Desktop/work/study/mcp-image-generator/dist/index.js" # 替换为你的路径]}}
}

重启Cursor使配置生效

测试效果

输入

Please generate a picture of an animal fox and save it to the directory /Users/admin/Desktop/work/study/mcp-image-generator/pictures with the filename fox.

Run tool，查看图片

参考

https://juejin.cn/post/7485267450880229402
https://www.cnblogs.com/foxhank/p/18378208
https://github.com/fengin/image-gen-server?tab=readme-ov-file
https://cursor.directory/mcp
https://zhuanlan.zhihu.com/p/27327515233
https://blog.csdn.net/m0_65096391/article/details/147570383