World Congress 2026 Europe - Virtual Stage Jul 2, 2026 Session details

Bridging AI and Nomad: a Go-based MCP Server for Cluster Control

Erik Koci

Tired of juggling terminals and runbooks during incidents? Discover how a Go-based MCP server allows AI assistants to securely manage HashiCorp Nomad clusters via natural language.

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#1 about 2 min

Bridging AI and cluster control with an MCP server

Integrating AI with infrastructure management streamlines cluster operations and reduces manual configuration tasks.

#2 about 2 min

Understanding the HashiCorp Nomad orchestrator capabilities

The Nomad orchestrator provides a flexible, single-binary alternative to Kubernetes for scheduling varied application workloads.

#3 about 3 min

Standardizing AI tool interactions with the Model Context Protocol

Implementing the Model Context Protocol unifies how AI models connect to disparate external tools and APIs.

#4 about 2 min

Guiding AI workflows using instruction-based skills

Providing specific playbook instructions ensures AI assistants execute complex tasks with consistent and predictable workflows.

#5 about 2 min

Reducing operational friction for on-call engineers

Consolidating troubleshooting commands into a single natural language interface eliminates frustrating context switching during production incidents.

#6 about 2 min

Architecting the connection between AI and Nomad

The custom server translates natural language prompts into direct API calls and returns actionable infrastructure statuses.

#7 about 2 min

Mapping the Nomad API to executable AI tools

Exposing the full orchestrator API through granular tool definitions enables comprehensive cluster management via conversational interfaces.

#8 about 2 min

Securing AI cluster access with API allowlists and tokens

Utilizing native orchestrator tokens alongside strict endpoint allowlists prevents unauthorized infrastructure modifications by autonomous agents.

#9 about 3 min

Evaluating design trade-offs in the Go-based server

Balancing comprehensive API coverage against language model processing latency requires careful Go interface abstraction.

#10 about 10 min

Demonstrating natural language cluster management and troubleshooting

Observing an AI assistant list active jobs and drain cluster nodes demonstrates the power of natural language operations.

#11 about 2 min

Applying key lessons for effective AI tool integrations

Writing clear tool descriptions and simplifying deployment processes are crucial steps for building successful AI infrastructure assistants.

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