MCP Servers Bring AI Reasoning to HPC Cluster Scheduling (2025-04-18)

The Model Context Protocol (MCP) defines a powerful and simple protocol for AI applications to interact with external tools. Its key benefit is modularity: any tool implementing an MCP server can be plugged into any AI application supporting MCP, allowing for seamless integration of specialized context and even control of external software.

Why is This Useful for HPC?

High Performance Computing (HPC) workload managers—like the venerable Open Cluster Scheduler (formerly Grid Engine)—must accommodate an incredible range of use cases. From desktops running a few sequential jobs, to massive clusters processing millions of jobs daily, requirements and configurations can look dramatically different. Admins often become translators, bridging the gap between complex user requests and the equally complex world of scheduler configurations, with diagnostics (like “why aren't my jobs running?”) rarely having a single, straightforward answer.

MCP Server for Open Cluster Scheduler

I just implemented an example MCP server for Open Cluster Scheduler for research and academic use. It enables an AI, like Claude, to translate high-level, natural language questions into low-level cluster operations and return formatted, accurate, and actionable information, potentially combined with other sources. The AI can answer "Why aren't my jobs running?" with cluster context, analyze configurations, generate job overviews, or spot patterns in job submissions.

Getting Started

The MCP server for Open Cluster Scheduler is easy to try in a containerized, simulated cluster. All code and documentation are available on GitHub.

Quickstart on MacOS with ARM / M chip

  1. Simulate a Cluster
     git clone https://github.com/hpc-gridware/go-clusterscheduler.git
 cd go-clusterscheduler
 make build && make simulate
    This launches a container with a fake (but realistic!) test cluster. Test with qhost and qsub.
  2. Launch MCP Server
     cd cmd/clusterscheduler-mcp
 go build
 ./clusterscheduler-mcp
    The MCP server opens port 8888, forwarded to your host.
  3. Connect Your AI
    • Use npx mcp-remote as a wrapper to connect tools like Claude (details in their docs).
    • Example config for Claude:
      {
  "mcpServers": {
    "gridware": {
      "command": "npx",
      "args": ["mcp-remote", "http://localhost:8888/sse"]
    }
  }
}
  4. Interact
    Once connected, use natural language to ask about job status, configuration advice, or request analyses.

Example Use Cases

  • Diagnose why jobs are not running with AI-aided reasoning.
  • Generate overviews of current and past jobs in tabular or summary form.
  • Clone and analyze entire cluster setups, creating a digital twin for testing or support.

Screenshots

Running jobs overview table:

Running Jobs Overview

Why is my job not running?

Why is my job not running?

Conclusion

MCP bridges the gap between complex system tooling and natural language AI assistants, making powerful cluster analysis, debugging, and administration accessible even to non-experts. With a growing ecosystem of thousands of MCP servers, and modern AI’s impressive reasoning abilities, troubleshooting and tuning HPC environments has never been easier. In the world of HPC clusters: Why not building a digital twin of your HPC clusters workload manager for getting getting insights, try changes, and test the impact with your LLMs help? The building blocks are all available.

For more details, see the project documentation. Contributions and feedback are always welcome!