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2025-03-31

用于提供分析的MCP SDK服务器的轻巧的打字稿中间件。捕获要求指标,性能数据和使用最小开销的使用模式。具有实时监视,可配置的数据收集和详细报告 - 全部具有完整类型的安全性。

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MCP Analytics Middleware

npm version npm downloads

A simple way to track and visualize how your MCP server is being used. See which tools are most popular, catch errors early, and understand your server's performance.

Features

  • 🔍 Track all tool calls and resource requests
  • 📊 See performance metrics and error rates
  • 🌐 Web dashboard for live analytics
  • 💾 SQLite database for persistent storage

Quick Start

  1. Install the package:
yarn add mcp-analytics-middleware
  1. Add it to your MCP server:
import { McpAnalytics } from 'mcp-analytics-middleware';

let server = new McpServer({ 
name: 'Sample MCP Server with Analytics',
version: '1.0.0'
});

const analytics = new McpAnalytics('analytics.db');

server = analytics.enhance(server); // override tool and resource function implementation to record usage in sqlite

Live Analytics

Want to see a dashboard for a Tyescript SDK MCP Server making use of this middleware? You can directly provide a live dashboard using

npx -p mcp-analytics-middleware web-viewer --db-path analytics.db

The web dashboard will open at http://localhost:8080 and show you live analytics!

You'll see:

  • Total tool calls and resource requests
  • Error rates and performance metrics
  • Most used tools and slowest operations

Example Implementations

Example implementatinos of the analytics middleware can be found.

  1. Dummy Caluclator Server Example src/server.ts
  2. Ethereum RPC MCP Server with Analytics server/index.ts_
  3. Forked Verision of the Google Maps MCP Server with additional Analytics Middleware src/google-maps/index.ts

Running with Inspector

If you're using the MCP Inspector, just add the analytics flag:

yarn inspector --analytics --db-path analytics.db

License

MIT

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    Reviews

    5 (1)
    Avatar
    user_LkxlCcdJ
    2025-04-16

    As a dedicated user of mcp-analytics-middleware, I highly recommend it for anyone looking to streamline their data analytics processes. Developed by the talented Phillip-Kemper, this middleware integrates seamlessly with various applications, enhancing your data management capabilities. The user-friendly interface and robust functionality have significantly improved my workflow. Check it out on GitHub: https://github.com/Phillip-Kemper/mcp-analytics-middleware.