tcs-mcp
Related Servers
Alternatives to tcs-mcp
No user-submitted related servers found.
Related Servers
- FlicenseAqualityCmaintenanceMCP server that integrates Claude with the Kyushu University supercomputer Genkai's TCS (PJM) job scheduler. It enables natural language control of job submission, status monitoring, deletion, resource queries, and reading output files.11-
- AlicenseNot gradedqualityBmaintenanceMCP server for the Fugaku supercomputer that enables natural language job submission, monitoring, file transfer, and command execution via AI agents like Claude Code.3MIT
- AlicenseNot gradedqualityCmaintenanceMCP server that converts SSH operations on training servers into AI-callable tools for GPU monitoring, job submission, file transfer, and more.1MIT
- FlicenseAqualityBmaintenanceAn MCP server that enables LLMs to execute Python code on GPU-accelerated compute nodes within SLURM-managed HPC environments. It bridges local clients to remote clusters by launching JupyterLab sessions via SLURM jobs to facilitate high-performance notebook-based computation.7-
- FlicenseNot gradedqualityDmaintenanceAn MCP server that gives AI coding assistants direct access to Slurm HPC clusters for job submission, file management, and shell access.3-
- AlicenseNot gradedqualityDmaintenanceA server implementing Model Coupling Protocol for HDF5 file operations, Slurm job management, hardware monitoring, and data compression.MIT
TDQS
Scored across 18 tools
Most tools have distinct purposes (jobs, files, resources, modules, etc.). However, validate_job and render_job both handle structured job definitions, creating minor ambiguity about which to use for validation only. submit_job also performs a preview, but its confirmation token makes its role clear.
The vast majority of tools follow a clear verb_noun pattern (list_jobs, get_job, submit_job, read_file, etc.). The only outlier is 'system_status', which uses noun_noun instead of a verb-based name, breaking the otherwise consistent convention.
With 18 tools, the set is slightly above the ideal 3-15 range, but the breadth of functionality (job management, file operations, system status, and the preview/confirm flow) justifies the count. It is not bloated to the point of being overwhelming.
The tool surface covers the core HPC workflow: job submission/validation/cancellation/statistics, file reading/writing/searching/transfer, resource and module queries, and system status. Minor gaps exist, such as no direct job output/error viewing (though read_file can access output paths) and no job modification (update/alter), but these are workable.