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MCP Analytics

@embeddedlayers

基本信息

分类

数据与分析

传输方式

stdio

作者

embeddedlayers

提交者

Mark Shors

关于 MCP Analytics

MCP server for data analytics — upload CSV files or connect Shopify, Stripe, GA4, and Search Console. 50+ statistical and ML tools including regression, clustering, time series, hypothesis testing, and customer analytics. Semantic tool discovery matches your question to the right

MCP Analytics 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "mcp-analytics": {
      "command": "npx",
      "args": [
        "@anthropic/mcp-analytics"
      ]
    }
  }
}

MCP Analytics 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。

点击上方按钮,在临时沙箱中运行此服务器并实时获取工具列表。

概览

What is Mcp Analytics?

Mcp Analytics is an MCP server for data analytics that integrates with Shopify, Stripe, WooCommerce, eBay, CSV files, and more. It lets users run statistical analysis, forecasting, and machine learning directly in Claude or Cursor by asking a natural language question and uploading data.

How to use Mcp Analytics?

Add the following JSON snippet to your MCP client config (Claude Desktop, Cursor, VS Code Continue Extension, or Claude Code): "command": "npx", "args": ["-y", "mcp-remote@latest", "https://api.mcpanalytics.ai/auth0"]. Restart your IDE, authenticate via OAuth2, then ask analytics questions in natural language.

Key features of Mcp Analytics

  • Natural language interface for analytics
  • Automated discovery of the right analytical approach
  • Supports regression, forecasting, clustering, and more
  • Interactive HTML reports with charts and AI insights
  • Connect live data from GA4 and Google Search Console
  • Zero setup, cloud-based, enterprise security with OAuth2

Use cases of Mcp Analytics

  • Analyze sales drivers from CSV exports
  • Segment customers and predict churn
  • Forecast next quarter’s revenue
  • Test hypotheses and experimental designs
  • Run statistical analysis on live GA4 data

常见问题

1

What tools does Mcp Analytics provide?

It offers a full suite: `discover_tools`, `tools_run`, `datasets_upload`, `connectors_list`, `reports_view`, `agent_advisor`, and more for end-to-end analytics.

2

How does authentication work?

Authentication uses OAuth2 via Auth0 with PKCE. On first use you’re prompted to authenticate, and all API calls are secured with OAuth 2.0 scoped permissions.

3

What data sources are supported?

You can upload any CSV/JSON/URL file, or connect live data from Google Analytics 4 and Google Search Console via native connectors.

4

How is my data handled?

Data is processed in isolated Docker containers with TLS 1.3 encryption. Processing is ephemeral (no data retention), and you can request deletion at any time.

5

How do I test the connection?

After installation, restart your IDE and look for “MCP Analytics” in the available tools. You can also test directly with: `npx -y mcp-remote@latest https://api.mcpanalytics.ai/auth0`.

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