Skip to main content
Glama
Sign In
agent-gpu-pool

agent-gpu-pool

by HarrisonYangKuang
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Related Servers

Alternatives to agent-gpu-pool

No user-submitted related servers found.

    Related Servers

    • agent-gpu-pool

      AlfredQuan
      A
      license
      B
      quality
      C
      maintenance
      Provides a persistent compute queue for AI agents, enabling them to submit hardware-aware GPU jobs, monitor execution, and retrieve results and artifacts across sessions via MCP tools.
      13
      MIT
    • jungle-grid-mcp-server

      jungle-grid-mcp-serverofficial

      Jungle-Grid
      A
      license
      A
      quality
      C
      maintenance
      Jungle Grid MCP Server lets AI agents submit, estimate, monitor, and retrieve logs for GPU workloads through Jungle Grid. It enables agentic execution for inference, training, fine-tuning, and batch jobs without manually choosing GPU providers or infrastructure.
      8
      12 npm
      4
      MIT

    • Jungle Grid MCP Server

      jaguarkyng
      A
      license
      A
      quality
      C
      maintenance
      Enables MCP-aware agents to estimate, submit, monitor, cancel, and retrieve artifacts from Jungle Grid workloads, supporting asynchronous AI execution, batch processing, training, fine-tuning, and access to logs and managed outputs.
      11
      12 npm
      MIT

    • GPU Lab MCP Server

      TheWWWorm
      A
      license
      Not graded
      quality
      C
      maintenance
      Enables MCP clients to submit, monitor, and manage jobs on shared NVIDIA GPUs, inspect logs and status, cancel work, and interact with isolated virtual displays through screenshots, window inspection, and input.
      Apache 2.0
    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    B3.3/5.0

    Scored across 14 tools

    Disambiguation5/5

    Each tool targets a distinct operation or resource, with clear separation between job management (submit, get, list, cancel, logs), artifact handling (fetch, retry, list), experiment records, and infrastructure overview (pool, workers, quotas). Even similar-sounding tools like list_jobs and list_ready_results are clearly differentiated by their purpose and description.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern (e.g., submit_job, list_jobs, cancel_job, fetch_artifact, sync_quotas). The verbs are clear and uniform across the set, making it easy to predict the action of each tool from its name.

    Tool Count5/5

    With 14 tools covering job lifecycle, artifact management, experiment recording, and cluster monitoring, the count feels well-scoped for a scientific computing MCP server. Each tool serves a distinct purpose without redundancy, and the number is within the ideal range for maintainability.

    Completeness5/5

    The tool set covers the full lifecycle: job submission, retrieval, listing, cancellation, logs; artifact collection, retrieval, and listing; experiment result recording and project runs; and infrastructure monitoring like worker lists and quota sync. This is a complete surface for managing computational experiments on a GPU pool.

    Maintenance

    ActivitySlowing
    ResponsivenessNo recent community issues

    Resources