Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
作者提供的说明(英文原文)
50+ pandas-powered tools for data loading, cleaning, visualization, and ML workflows
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 …”。
适用于网页版、桌面版和手机版 Claude:
https://mcp.statscompass.io/mcp打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"stats-compass": {
"command": "uvx",
"args": [
"stats-compass-mcp"
]
}
}
}在终端中运行这条命令
claude mcp add --transport http stats-compass https://mcp.statscompass.io/mcpclaude mcp add stats-compass -- uvx stats-compass-mcp粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"stats-compass": {
"url": "https://mcp.statscompass.io/mcp"
}
}
}粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"stats-compass": {
"command": "uvx",
"args": [
"stats-compass-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": {
"stats-compass": {
"type": "http",
"url": "https://mcp.statscompass.io/mcp"
}
}
}粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"stats-compass": {
"type": "stdio",
"command": "uvx",
"args": [
"stats-compass-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。
ChatGPT 只能连接互联网上的服务器:
https://mcp.statscompass.io/mcp菜单名称可能因套餐和版本不同而略有差异。
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。
服务器要求先登录,才会列出它的工具。
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8日
注册名称已通过 GitHub 账户 oogunbiyi21 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
在 315 毫秒内响应(HTTP 401)
声明支持 OAuth 登录,你的 AI 应用可以带你去登录
尚未检查;我们会在检查端点时读取工具列表
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 3 项适用的基础检查中通过了 3 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。
两种方式都可以:安装在你的电脑上,或者连接云端版本。
GitHub 仓库最后更新于–,最新版本(0.3.37)于9天前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。它通过 URL 连接,所以接受远程 MCP 服务器的应用也可以使用,例如开启开发者模式的 ChatGPT。
同一分类中有:Spec Driven Development, Remote Desktop Commander, Filesystem MCP
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 that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Filesystem MCP server for AI assistants.
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。
Turn your LLM into a data analyst. Multiple data science tools via MCP.
pip install stats-compass-mcp
stats-compass-mcp install --client claude
stats-compass-mcp install --client vscode
claude mcp add stats-compass -- uvx stats-compass-mcp run
Note: The first connection may fail while
uvxdownloads the package. If this happens, disable and re-enable Stats Compass in your MCP settings — subsequent connections will be instant.
Restart your client and start asking questions about your data.
| Category | Examples |
|---|---|
| Data Loading | Load CSV/Excel, sample datasets, list DataFrames |
| Cleaning | Drop nulls, impute, dedupe, handle outliers |
| Transforms | Filter, groupby, pivot, melt, encode, add columns |
| EDA | Describe, correlations, hypothesis tests, data quality |
| Visualization | Histograms, scatter, bar, ROC curves, confusion matrix |
| ML Workflows | Classification, regression, time series forecasting |
| Sales | Is this week expected, a real change or noise? What does each month usually bring? What did a promotion add, and did sales dip after it? Daily or weekly sales, per retailer or product, with a promotion calendar |
Run stats-compass-mcp list-tools to see all available tools.
Start your message with "Use stats compass to..." — this tells the AI to use the Stats Compass tools instead of trying to write code or use other methods.
Use stats compass to load ~/Downloads/sales.csv and run EDA on it
Use stats compass to find my CSV files in Downloads Use stats compass to clean the dataset and handle missing values Use stats compass to create a histogram of the price column Use stats compass to test if there's a significant difference in scores between group A and B Use stats compass to train a classification model to predict churn Use stats compass to load sell_out.xlsx and tell me if last week's sales at each retailer were unusual, leaving out the promotions in promo_calendar.csv Use stats compass to measure what the June promotion added at Tesco, using promo_calendar.csv
Tip: Without this prefix, some AI clients may try to write Python code or use shell commands instead of the Stats Compass tools — especially for tasks like finding files on your machine.
Local mode: Start with "Use stats compass to load..." and provide the file path or folder.
Use stats compass to load the CSV at ~/Downloads/sales.csv
Use stats compass to find my data files in ~/Documents
Remote/HTTP mode: Use the upload feature (see below).
For Docker deployments or multi-client setups:
stats-compass-mcp serve --port 8000
serve binds 127.0.0.1 by default. To accept connections from other machines, set a bearer token of at least 16 characters and bind a public address:
export STATS_COMPASS_AUTH_TOKEN="$(openssl rand -hex 24)"
export STATS_COMPASS_SECRET_KEY="$(openssl rand -hex 32)" # encrypts upload/download links stats-compass-mcp serve --host 0.0.0.0 --port 8000
Clients send Authorization: Bearer . Without a token the server refuses a public address; --no-auth overrides that, with a warning, for a network you trust. Each session reads only its own uploads and writes only its own exports. Upload and download links carry an encrypted token that expires (downloads after 24 hours, STATS_COMPASS_DOWNLOAD_TTL_SECONDS), never the session id.
A session idle for 24 hours is removed, with its data, models and exported files (STATS_COMPASS_SESSION_IDLE_HOURS; 0 turns it off). A session in the middle of a tool call is never removed. A server that embeds the tools runs its own sweep with SessionManager.evict_idle(max_idle).
When running remotely, users can upload files via browser:
You: I want to upload a file
AI: Open this link to upload: http://localhost:8000/upload?session_id=abc123
[Upload in browser]
You: I uploaded sales.csv AI: ✅ Loaded sales.csv (1,000 rows × 8 columns)
Export DataFrames, plots, and trained models:
You: Save the cleaned data as a CSV
AI: ✅ Saved. Download: http://localhost:8000/exports/.../cleaned_data.csv
VS Code (native HTTP support):
{
"servers": { "stats-compass": { "url": "http://localhost:8000/mcp" } } }
Claude Desktop (via mcp-proxy):
{
"mcpServers": { "stats-compass": { "command": "uvx", "args": ["mcp-proxy", "--transport", "streamablehttp", "http://localhost:8000/mcp"] } } }
docker run -p 8000:8000 -e STATS_COMPASS_SERVER_URL=https://your-domain.com stats-compass-mcp
| Client | Status |
|---|---|
| Claude Desktop | ✅ Recommended |
| VS Code Copilot | ✅ Supported |
| Claude Code CLI | ✅ Supported |
| Cursor | ✅ Supported |
| GPT / Gemini | ⚠️ Partial |
| Variable | Default | Description |
|---|---|---|
STATS_COMPASS_PORT | 8000 | Server port |
STATS_COMPASS_SERVER_URL | http://localhost:8000 | Base URL for upload/download links |
STATS_COMPASS_MAX_UPLOAD_MB | 50 | Max upload size |
See CONTRIBUTING.md for development setup.
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