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Looking-Glass-MCP

by Jackie-shi
README.md6.34 kB
# Looking-Glass-MCP The first Looking Glass Model Context Protocol (MCP) server! 🎉 ## Overview Looking-Glass-MCP is a revolutionary MCP server that provides network probing capabilities through Looking Glass (LG) vantage points. This tool allows you to perform network diagnostics and measurements from multiple global locations using a simple, standardized interface. ## Features - **Multi-VP Probing**: Execute network commands from multiple Looking Glass vantage points simultaneously - **Auto VP Selection**: Automatically select the optimal number of vantage points for your measurements - **Comprehensive Commands**: Support for ping, BGP route lookups, and traceroute operations - **Global Coverage**: Access to Looking Glass servers worldwide - **Async Operations**: Built with async/await for efficient concurrent operations - **Error Handling**: Robust error handling and timeout management ## Available Tools ### `lg_probing_user_defined` Send probing commands to a target IP using a specific list of LG vantage points. **Parameters:** - `vp_id_list`: List of Looking Glass VP identifiers - `cmd`: Command type (`ping`, `show ip bgp`, `traceroute`) - `target_ip`: Destination IP address for probing ### `lg_probing_auto_select` Send probing commands using automatically selected vantage points. **Parameters:** - `vp_num`: Number of vantage points to use - `cmd`: Command type (`ping`, `bgp`, `traceroute`) - `target_ip`: Destination IP address for probing ### `list_all_lgs` Retrieve information about all available Looking Glass vantage points. ## Requirements - Python 3.13+ - httpx >= 0.28.1 - mcp[cli] >= 1.9.4 ## Installation ```bash pip install -r requirements.txt ``` ## Usage Example: CDN Performance Analysis This example demonstrates how to use Looking-Glass-MCP for CDN performance optimization by analyzing network performance to Google's DNS service (8.8.8.8) from multiple global locations. ### Step 1: List Available Vantage Points ```python # Get all available Looking Glass vantage points result = await list_all_lgs() ``` ### Step 2: Select Global Vantage Points We selected 5 diverse global locations for comprehensive analysis: - **US (Seattle)**: VP ID 71 - North America - **Switzerland**: VP ID 164 - Europe - **Singapore**: VP ID 62 - Asia-Pacific - **Australia (Sydney)**: VP ID 125 - Oceania - **Brazil (Campo Grande)**: VP ID 108 - South America ### Step 3: Execute Network Probing ```python # Run ping tests from 5 global locations ping_results = await lg_probing_user_defined( vp_id_list=["71", "164", "62", "125", "108"], cmd="ping", target_ip="8.8.8.8" ) # Run traceroute analysis traceroute_results = await lg_probing_user_defined( vp_id_list=["71", "164", "62", "125", "108"], cmd="traceroute", target_ip="8.8.8.8" ) # Analyze BGP routing information bgp_results = await lg_probing_user_defined( vp_id_list=["71", "164", "62", "125", "108"], cmd="bgp", target_ip="8.8.8.8" ) ``` ### Step 4: LLM-Generated Analysis Results > **📊 Example Output**: The following analysis was automatically generated by an LLM using the Looking-Glass-MCP tool results. This demonstrates how AI can transform raw network data into actionable business insights. <details> <summary><strong>🤖 LLM Analysis Output</strong> (Click to expand)</summary> #### 🌍 Global Network Performance Summary | Location | Country | Latency (avg) | Packet Loss | Performance Rating | |----------|---------|---------------|-------------|-------------------| | **Seattle, US** | US | ~1-4ms | 0% | ⭐⭐⭐⭐⭐ Excellent | | **Switzerland** | CH | ~1ms | 0% | ⭐⭐⭐⭐⭐ Excellent | | **Singapore** | SG | ~2.1ms | 0% | ⭐⭐⭐⭐⭐ Excellent | | **Sydney, Australia** | AU | ~0.54ms | 0% | ⭐⭐⭐⭐⭐ Outstanding | | **Campo Grande, Brazil** | BR | ~19.8ms | 0% | ⭐⭐⭐ Good | #### 🔍 Key Findings **Best Performance:** - **Australia (Sydney)**: Exceptionally low latency (~0.54ms) - direct Google peering - **Switzerland & US**: Sub-millisecond to low single-digit latency **Routing Analysis:** - **Australia**: 6 hops, direct peering with Google infrastructure - **Singapore**: 4 hops, excellent Google edge presence - **Switzerland**: 4 hops, optimal European routing - **Brazil**: 15+ hops, complex ISP routing causing higher latency **BGP Intelligence:** - All locations show AS15169 (Google) as origin - Multiple path redundancy visible - Local peering arrangements in major markets #### 🎯 CDN Optimization Recommendations **Immediate Actions:** 1. **Prioritize APAC**: Australia and Singapore show excellent performance 2. **European Expansion**: Switzerland performance suggests good connectivity 3. **Brazil Improvement**: Higher latency indicates need for local presence **Strategic Recommendations:** 1. **Multi-CDN Strategy**: Deploy edge servers in Sydney, Singapore, and Europe 2. **Peering Optimization**: Establish direct peering with major ISPs in Brazil 3. **Performance Monitoring**: Use these 5 locations as baseline monitoring points **Expected Impact:** - **Australia/Singapore**: Maintain sub-2ms response times - **Europe**: Target sub-5ms response times - **Brazil**: Improve from 20ms to <10ms with local presence </details> > **💡 Key Insight**: This example shows how Looking-Glass-MCP enables AI assistants to automatically analyze complex network data and provide actionable business recommendations - transforming raw technical metrics into strategic insights. ### Real-World Applications This Looking Glass MCP tool is perfect for: - **CDN Performance Optimization**: Analyze global performance patterns - **Network Troubleshooting**: Identify routing issues from multiple perspectives - **DDoS Detection**: Monitor traffic patterns across vantage points - **Competitive Analysis**: Benchmark against competitor infrastructure - **SLA Monitoring**: Validate service level agreements globally - **Research**: Academic studies on internet topology and performance ## Getting Started 1. Install dependencies 2. Configure your MCP client to use Looking-Glass-MCP 3. Start analyzing global network performance! The power of Looking Glass combined with MCP's standardized interface makes network analysis accessible and actionable for any application requiring global network intelligence.

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