Anthropic MCP Server for generating structured CSV files from natural language descriptions
Connect Structurize MCP to Claude, Cursor or any other MCP client and it stops being a tab you switch to. Anthropic MCP Server for generating structured CSV files from natural language descriptions. The structurize mcp mcp server is what makes that connection.
Structurize-MCP 是一个基于 Model Context Protocol 的服务，可以根据自然语言描述生成结构化的 CSV 文件。它使用 Google Gemini AI 来解析和生成数据。
structurize-mcp on npm is all you need. Most clients run it directly, so configuration is a few lines and a restart.
Configuration is passed through the environment: YOUR_GEMINI_API_KEY. Treat anything key-shaped as a real credential — scope it to the minimum the server needs, and rotate it if it ever lands in a shared config.
Among the AI and media services options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. It is maintained by pcliu; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
如果是从源码运行，可以使用：
```json
{
"mcpServers": {
"csv-generator": {
"command": "node",
"args": [
"/absolute/path/to/structurize-mcp/build/index.js",
"--gemini-api-key",
"YOUR_GEMINI_API_KEY_HERE",
"--csv-dir",
"/absolute/path/to/csv_output_directory"
]
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| YOUR_GEMINI_API_KEY | Credential the server authenticates with. | Yes |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.