Supabase
MCP server for interacting with the Supabase platform
作者提供的说明(英文原文)
Fake, mock and test data generator: CSV, JSON, SQL from a .tdc config, made locally by TDCv2.
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
你需要
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"tdcv2": {
"command": "npx",
"args": [
"-y",
"tdcv2-mcp"
]
}
}
}在终端中运行这条命令
claude mcp add tdcv2 -- npx -y tdcv2-mcp粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"tdcv2": {
"command": "npx",
"args": [
"-y",
"tdcv2-mcp"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"tdcv2": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"tdcv2-mcp"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。
你的选择通过 Cookie 保存在这台设备上,无需账户。
这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日
注册名称已通过 GitHub 账户 NickLiapin 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
它在你的电脑上运行;我们只能读取互联网端点的工具列表
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
GitHub 仓库最后更新于–,最新版本(0.1.7)于3天前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。
同一分类中有:Supabase, MongoDB MCP Server, Elasticsearch MCP Server
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
MCP server for interacting with the Supabase platform
MongoDB Model Context Protocol Server
MCP server for interacting with Elasticsearch
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
A data generator for your coding agent: fake, mock, synthetic and test data with TDCv2 — realistic names, emails, addresses, phone numbers, IBANs and more. The agent writes a small .tdc config, the TDCv2 engine generates the rows on your machine, and the same config gives the same file, byte for byte, every time you run it. CSV, JSON, SQL inserts or any text format; fields that agree with each other; exact shares; related tables; data packs for 95 languages and 198 countries. Think Faker, but for whole datasets: database seeds, test fixtures, demo data.
Two parts, one version:
skills/tdcv2);tdcv2-mcp — the same, as tools: check a config, generate, find and install data packs, summarise a file, read the docs (mcp/).Claude Code and Cowork — the plugin brings both the skill and the server. The simplest way: in claude.ai or the Claude app, Customize → Plugins → Discover, find TDCv2 — Fake & Test Data Generator, Add; Claude Code picks it up at the next session and keeps it updated. Or from this repository's marketplace:
claude plugin marketplace add NickLiapin/tdcv2-agents
claude plugin install tdcv2@tdcv2
Install one way, not both — otherwise the skill loads twice and two servers start.
Codex, Copilot, Gemini CLI, Cursor and other agents that read skills — put the skill where your agent looks for it:
npx -y tdcv2-mcp@0.1.7 install-skill # ~/.claude/skills/tdcv2
npx -y tdcv2-mcp@0.1.7 install-skill --agents # ~/.agents/skills/tdcv2 npx -y tdcv2-mcp@0.1.7 install-skill --project # ./.claude/skills/tdcv2 npx -y tdcv2-mcp@0.1.7 install-skill --dir
Any MCP client (Claude Desktop, Cursor, VS Code…):
{ "mcpServers": { "tdcv2": { "command": "npx", "args": ["-y", "tdcv2-mcp@0.1.7"] } } }
Claude Desktop starts servers in /: add "env": {"TDCV2_PROJECT": "/path/to/project"} to that entry so the files have a folder to go to. On Windows use "command": "npx.cmd" if the client cannot start npx. Everything needs Node.js 20 or newer.
Step by step for each agent and client: TDC for AI agents.
Then ask in plain words — *"500 customers from 20 countries as CSV"*, *"a pytest test for validate_user on 50 different users"* — and the agent takes it from there: writes the config, checks it with the engine, generates, and hands you the file together with the config that reproduces it.
The engine runs locally. Neither the skill nor the server sends your data or your configs anywhere. The server reads .tdc files and writes generated files only inside the project folder it is started in.
Network access, all of it:
npx -y tdcv2-mcp@0.1.7, its own version (scripts/mcp-launch.mjs), so the first start downloads that exact version and its dependencies, the TDCv2 engine among them, from the npm registry;npx -y tdcv2-mcp@0.1.7 … in the commands above does the same, and so does npx -y tdcv2@0.3.7 …, the exact engine version the agent runs;raw.githubusercontent.com;tdcv2 pack add (through the server: tdc_pack_add, which your client asks you to allow); sends the named values to that URL — only if someone writes such a config.Nothing else is fetched, and nothing is sent. The full policy: PRIVACY.md.
This release, 0.1.7, is checked against TDCv2 0.3.7. The skill's reference is built from the engine's documentation for that version; with another engine version the agent reads that version's pages, and the engine's own check has the last word.
MIT