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 identifierscmd: 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 usecmd: 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
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
# 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
# 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.
🤖 LLM Analysis Output (Click to expand)
🌍 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:
- Prioritize APAC: Australia and Singapore show excellent performance
- European Expansion: Switzerland performance suggests good connectivity
- Brazil Improvement: Higher latency indicates need for local presence
Strategic Recommendations:
- Multi-CDN Strategy: Deploy edge servers in Sydney, Singapore, and Europe
- Peering Optimization: Establish direct peering with major ISPs in Brazil
- 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
💡 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
- Install dependencies
- Configure your MCP client to use Looking-Glass-MCP
- 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.