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

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.

<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.