$ loading_
Submit, manage, and schedule HPC workloads through Flux Framework MCP tools.
This is an open-source MIT-licensed MCP tool with no declared secrets or remote endpoints, and the provided materials do not show clear high-risk red flags. However, it is designed for local job submission and resource scheduling, so it should be treated as a system-operating tool and granted runtime permissions cautiously.
The materials explicitly state that no keys or environment variables are required, and there is no indication that users must provide API tokens, account credentials, or other sensitive secrets; based on the available information, credential exposure and abuse risk appears low.
The materials list no remote endpoint hosts, and the README does not describe sending data to external services or connecting to third-party APIs; no explicit outbound data path is evident from the provided information.
The system flags this tool as executes-code, and its description explicitly covers job submission, management, and resource scheduling, which typically implies the ability to trigger process or task execution on a local or cluster environment. This is a normal capability for this class of tool and warrants caution because it can affect compute resources and job lifecycle, but it is not by itself a high-risk red flag.
The description is centered on HPC jobs and scheduling, which commonly involves access to job definitions, runtime state, and related local or cluster resource information; however, the materials do not specify exact file paths, data scope, or any access beyond the stated purpose. At this stage, it should be treated cautiously as having potential access to job-related data.
Positive factors include an auditable open-source repository and an MIT license; however, the source is a third-party registry, community adoption is 0 stars, maintenance status is unknown, and the provided documentation is minimal, making implementation and dependency risks harder to assess. Overall, this is better rated as caution rather than high risk.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "flux-mcp" yet — see the docs or source repo.
Use flux-mcp to submit an HPC batch job: run /home/user/run_sim.sh, request 8 CPUs, 1 GPU, 64GB memory, with a 4-hour time limit, and return the job ID.
Returns key submitted job details such as job ID, resource request, queue status, and expected execution info.
Use flux-mcp to list all my queued and running jobs, grouped by status; if any jobs failed, show failure reasons and provide actionable retry suggestions.
Outputs a status-organized job list, failure summaries, and suggested next management or retry actions.
I have an MPI simulation job expected to run for 2 hours. Use flux-mcp to suggest suitable node count, CPU cores, memory, and scheduling parameters to improve utilization and reduce queue time.
Provides a better resource request and scheduling parameter recommendation, with reasons for the proposed configuration.
Generate high-quality images with Flux AI for creative and visual content work.
Create Flux 2 jobs, track status, and check pricing for integrations.
Control agent workflows with stateful primitives and persisted execution facts.
Let AI access Redmine projects, issues, and time data in natural language.
Build MCP servers with embedded reasoning for efficient complex task handling.
Use MCP to create Flux Kontext tasks, poll status, and check pricing.