MCP server
by terryso
MetaTag Genie uses AI to write standardized metadata to image formats like HEIC and PNG, helping you automate tagging an
Writes metadata (tags, descriptions, people, locations) directly to image files in HEIC, PNG, and JPG formats, making them searchable via macOS Spotlight.
MetaTag Genie is a community-built MCP server published by terryso that provides AI assistants with tools and capabilities via the Model Context Protocol. MetaTag Genie uses AI to write standardized metadata to image formats like HEIC and PNG, helping you automate tagging an It is categorized under file systems. This server exposes 1 tool that AI clients can invoke during conversations and coding sessions.
You can install MetaTag Genie in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
MIT
MetaTag Genie is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Read, analyze, and understand files in your project
Example
Summarize README, analyze code structure, find TODO comments across codebase
Navigate large codebases 5x faster, understand projects quickly
Create, move, rename, and organize files based on natural language instructions
Example
Organize downloads by file type, rename files following convention, batch process images
Save hours on manual file organization
Search files for patterns, extract data, find information across directories
Example
Find all config files with API keys, extract emails from documents, search logs for errors
Find information instantly instead of manual grep/find
Share your MCP server with the developer community
I recommend MetaTag Genie for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
According to our notes, MetaTag Genie benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
MetaTag Genie reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
MetaTag Genie has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
We evaluated MetaTag Genie against two servers with overlapping tools; this profile had the clearer scope statement.
Strong directory entry: MetaTag Genie surfaces stars and publisher context so we could sanity-check maintenance before adopting.
Useful MCP listing: MetaTag Genie is the kind of server we cite when onboarding engineers to host + tool permissions.
Strong directory entry: MetaTag Genie surfaces stars and publisher context so we could sanity-check maintenance before adopting.
We wired MetaTag Genie into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
Useful MCP listing: MetaTag Genie is the kind of server we cite when onboarding engineers to host + tool permissions.
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MetaTag Genie 是一个 macOS Stdio MCP 服务，专为写入图片元数据以增强 Spotlight 搜索而设计。该服务可被 AI 代理或其他需要本地管理图片元数据的应用程序调用，通过标准输入输出（Stdio）与客户端通信，提供符合 MCP (Machine Comprehension Protocol) 规范的接口。
writeImageMetadata MCP Toolexiftool-vendored，它会自动管理 ExifTool 的依赖要自动将 MetaTag Genie 安装到 Claude Desktop，请使用 Smithery：
npx -y @smithery/cli install @terryso/metatag_genie --client claude
# 全局安装
npm install -g metatag-genie
使用全局安装后，可以通过以下命令直接运行：
metatag-genie
或者，您可以不安装直接使用npx运行：
npx metatag-genie
这种方式不需要全局安装，也是AI代理等客户端调用的推荐方式。
# 1. 克隆仓库
git clone <repository-url>
cd metatag-genie
# 2. 安装依赖
npm install
# 或者: yarn install
# 3. 编译 TypeScript 代码
npm run build
# 或者: yarn build
# 4. 本地链接（可选，用于测试npx和命令行调用）
npm link
安装包后，可以直接通过npx运行：
npx metatag-genie
这种方式不需要全局安装包，适合作为AI代理或其他客户端的调用方式。
开发过程中，可以直接使用TypeScript源码运行服务：
npm run start:dev
此命令使用ts-node直接运行TypeScript代码，无需预先编译，适合快速开发和调试。
构建后，可以通过以下命令运行编译好的JavaScript代码：
npm start
# 或者直接: node dist/main.js
服务启动后会监听标准输入输出（Stdio），等待MCP客户端连接并发送JSON-RPC消息。
AI代理（如Cursor）或其他客户端需要在其配置中指定命令的完整路径：
/usr/local/bin/node /path/to/metatag-genie/dist/main.js/usr/local/bin/npx metatag-genie注意：本服务不监听网络端口，仅通过标准输入输出通信。
在Cursor编辑器中，可以通过以下步骤集成MetaTag Genie：
.cursor/mcp.json文件{
"mcpServers": {
"MetaTagGenie": {
"command": "npx",
"args": [
"-y",
"metatag-genie"
]
}
}
}
配置完成后，Cursor中的AI助手将能够使用MetaTag Genie提供的writeImageMetadata工具，直接为图片添加元数据，增强Spotlight搜索体验。
通信基于 Stdio 上的 JSON-RPC 2.0 协议。
客户端需要先发送 initialize 请求，服务器响应 InitializeResult，然后客户端发送 initialized 通知完成握手。
JSON-RPC 请求示例:
{
"jsonrpc": "2.0",
"id": "request-id-123",
"method": "writeImageMetadata",
"params": {
"filePath": "/Users/username/Pictures/photo.jpg",
"metadata": {
"tags": ["Vacation", "Beach"],
"description": "Sunset view from the hotel.",
"people": ["Alice", "Bob"],
"location": "Hawaii, USA"
},
"overwrite": true
}
}
JSON-RPC 成功响应示例:
{
"jsonrpc": "2.0",
"id": "request-id-123",
"result": {
"success": true,
"filePath": "/Users/username/Pictures/photo.jpg",
"message": "Metadata successfully written."
}
}
注意：实际使用时 filePath 需要是有效的绝对路径。
完整的 Tool 参数、返回值和错误代码定义，请参阅 MCP Tools 定义文档。
# 运行所有单元测试和集成测试
npm test
# 或者: yarn test
# 在监视模式下运行测试
npm run test:watch
# 或者: yarn test:watch
# 运行测试并生成覆盖率报告 (输出到 coverage/ 目录)
npm run test:cov
# 或者: yarn test:cov
关于不同测试层级的更多信息，请参阅 测试策略文档。
项目使用 ESLint 和 Prettier 强制代码风格，详细规范请参阅 编码规范文档。
请在提交代码前运行以下命令：
# 检查代码风格
npm run lint
# 自动格式化代码
npm run format
master 分支用于发布稳定版本master 创建特性分支（例如 feature/add-png-support 或 fix/handle-exiftool-error）master 分支如需报告 Bug 或提出功能建议，请在项目的 Issue Tracker 中创建新的 Issue，并提供尽可能详细的信息。
本项目使用GitHub Actions进行持续集成和自动发布：
提交PR时请确保通过所有CI检查。如需发布新版本：
package.json中的版本号本项目采用 MIT 许可证。详情请参阅项目根目录下的 LICENSE 文件。
Generate boilerplate files, apply templates, create project structures
Example
Create React component with tests and styles, generate OpenAPI spec, scaffold new project
Eliminate repetitive file creation work
Prerequisites
Time Estimate
10-20 minutes including configuration
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
MCP server provides file I/O operations (read, write, search, metadata) as tools Claude can invoke with natural language instructions.
Protocols
Compatibility
✓ Use when
Use for code analysis, file organization, content search, template generation, and automating repetitive file operations. Best for local development workflows.
✗ Avoid when
Avoid for system-critical files, sensitive credentials, production environments, or when file integrity is paramount. Don't use on files you can't afford to lose.