Let AI Explain traceroute with the Laws of Physics

I developed an open-source tool called PacketVoyage that transforms the often tedious traceroute output into an engaging narrative about physics, geography, and undersea cable networks. PacketVoyage functions as both an AI Agent (LLM) and an MCP server, providing educational analysis of network traceroutes with a focus on fiber-optic physics, verification, and storytelling.

Notably, PacketVoyage operates without relying on any external commercial APIs or bundled copyright data; it relies solely on fundamental physical laws and detective insights.

The architecture of PacketVoyage is built around two key layers: the AI Agent (LLM) and the MCP Server, each serving a distinct purpose in the workflow. The AI Agent is responsible for interpreting the traceroute results and explaining them in an accessible manner, while the MCP Server handles the technical aspects of the process, such as analyzing the data and executing the necessary computations.

When examining a traceroute, PacketVoyage identifies several critical elements. For instance, the asterisks (*) in certain hops represent rate limiting on the router CPU, which controls the rate of diagnostic ICMP responses rather than indicating actual packet loss. The delays between hops, such as the significant 86.4 ms increase from Hop 2 to Hop 5, can be explained by the speed of light in fiber-optic cables.

Traversing the Pacific Ocean via the Juno Submarine Cable, which spans approximately 8,335 kilometers, results in a theoretical speed-of-light latency of around 83 ms. The apparent discrepancy between this theoretical limit and the actual observed latency underscores the complexities of real-world network behavior.

PacketVoyage also highlights hidden identifiers in hostnames, such as "tokyo-hnd" (Haneda Airport) and "sjc-core" (San Jose), which reveal the specific undersea cables used for data transmission. This level of detail provides valuable insights for students and engineers alike, shedding light on the intricate workings of network routing and the physical principles that govern data transmission across vast distances.

To utilize PacketVoyage, one can integrate it with AI agents like Claude or any MCP-enabled agent that supports the PacketVoyage skill. By simply feeding a traceroute output to the AI agent, users receive a comprehensive explanation that combines technical analysis with an engaging narrative. This approach not only demystifies the intricacies of traceroute outputs but also enhances understanding of the underlying physical phenomena that influence network performance.

For those interested in exploring PacketVoyage further, the source code is available on GitHub at europeanplaice/packetvoyage.