Uploaded March 2026 | Updated September 2026, 2 weeks ago
#VoxxedDaysCERN26
At CERN’s Large Hadron Collider (LHC), every minute of downtime is precious. During technical stops, hundreds of specialists perform maintenance and upgrades before physics runs resume. Coordinating these interventions requires precise, conflict-free scheduling - a complex and time-critical process.
During the AI for Scientific Writing and Coding Assistants Hackathon, we built a prototype Model Context Protocol (MCP) server to assist LHC coordinators in planning and analyzing linear schedules. The system bridges the LHC scheduling tools’ APIs with a CERN-hosted large language model, enabling coordinators to interact with complex planning data in plain English. Through this interface, the MCP can perform automatic analyses such as detecting potential conflicts, validating resource and timing constraints, and gathering contextual information about interventions.
By combining structured scheduling data with natural language understanding, the prototype demonstrates how AI-assisted tools can enhance situational awareness and streamline planning quality checks. This talk explores the motivation, design, and lessons learned from integrating MCP with existing LHC workflows - showcasing how lightweight AI infrastructure can empower experts to coordinate maintenance and upgrades in one of the world’s most complex scientific machines.
#VoxxedDaysCERN26
At CERN’s Large Hadron Collider (LHC), every minute of downtime is precious. During technical stops, hundreds of specialists perform maintenance and upgrades before physics runs resume. Coordinating these interventions requires precise, conflict-free scheduling - a complex and time-critical process.
During the AI for Scientific Writing and Coding Assistants Hackathon, we built a prototype Model Context Protocol (MCP) server to assist LHC coordinators in planning and analyzing linear schedules. The system bridges the LHC scheduling tools’ APIs with a CERN-hosted large language model, enabling coordinators to interact with complex planning data in plain English. Through this interface, the MCP can perform automatic analyses such as detecting potential conflicts, validating resource and timing constraints, and gathering contextual information about interventions.
By combining structured scheduling data with natural language understanding, the prototype demonstrates how AI-assisted tools can enhance situational awareness and streamline planning quality checks. This talk explores the motivation, design, and lessons learned from integrating MCP with existing LHC workflows - showcasing how lightweight AI infrastructure can empower experts to coordinate maintenance and upgrades in one of the world’s most complex scientific machines.
