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flameox

by morluto
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    • io.github.morluto/flameox

      HelgaRoux
      F
      license
      Not graded
      quality
      A
      maintenance
      Enables coding agents to coordinate local profilers, benchmark tools, trace processors, and explicit targets into bounded runtime evidence, including observed inputs, execution provenance, typed findings, coverage, and limitations. It supports analysis and capture through named MCP tools without workspace setup or project configuration.
      -

    • io.github.morluto/flameox

      brobert3335
      F
      license
      Not graded
      quality
      A
      maintenance
      Enables coding agents to coordinate local profilers, benchmark tools, trace processors, and direct targets, turning explicit artifacts or commands into bounded runtime evidence with provenance, coverage, and limitations. Exposes named analysis and capture tools through MCP without workspace setup, with optional durable preservation.
      -

    • io.github.morluto/flameox

      brooks2882
      F
      license
      Not graded
      quality
      A
      maintenance
      Enables coding agents to coordinate profilers, benchmark tools, trace processors, and direct local targets to capture bounded runtime evidence, analyze artifacts, and inspect preserved evidence with provenance, coverage, and limitations.
      -

    • flameox

      lMarino48
      F
      license
      Not graded
      quality
      A
      maintenance
      Enables coding agents to run explicit local analysis and capture operations, turning artifacts or commands into bounded runtime evidence with provenance, coverage, limitations, and optional durable preservation. It coordinates profilers, benchmark tools, trace processors, and direct targets for investigating hotspots in application/native code, GPU kernels, and inference stacks.
      -

    • io.github.morluto/flameox

      Thu0273
      F
      license
      Not graded
      quality
      B
      maintenance
      Enables agents to run bounded local profiling, benchmarking, and trace analysis on explicit artifacts or commands, returning typed evidence with provenance, coverage, limitations, and recovery actions. It also exposes optional evidence preservation and inspection through MCP tools.
      -

    • io.github.morluto/flameox

      BrianDabrowski64376
      F
      license
      Not graded
      quality
      A
      maintenance
      Enables coding agents to turn explicit artifacts or commands into bounded local runtime evidence from profilers, benchmarks, trace processors, and direct targets, with typed provenance, coverage, and optional durable preservation. It exposes analysis and capture tools for tracing, profiling, and investigating hotspots in application and native code, GPU kernels, and inference stacks.
      -
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    Shareable, User-Owned Context BackendPeople, applications, and agents read, write, and coordinate together in one context lake. Reach it over MCP, a CLI, a Python SDK, or REST.fulcradynamics.comAd

    TDQS

    C2.9/5.0

    Scored across 111 tools

    Disambiguation4/5

    Most tools name an exact resource and action, making pairs like list_runs/get_run or plan_capture/execute_capture_plan easy to tell apart. However, the sheer number of get_*, list_*, extract_*, and analyze_* tools, plus near-neighbor pairs like compare_run_sets/compare_kernel_validation, introduces real selection risk.

    Naming Consistency4/5

    The dominant verb_noun snake_case convention is consistent across plan_*, extract_*, list_*, get_*, and analyze_* tools. Minor deviations such as workspace_status, workload_configuration_status, extract_memray behaving like a start operation, and get_native_viewer_plan keep it from a perfect score.

    Tool Count1/5

    111 tools is an extreme surface for any MCP server, far beyond a focused, well-scoped toolset. Even if each tool serves a distinct profiling sub-domain, an agent would have to navigate an overwhelming and impractical number of choices.

    Completeness4/5

    The toolkit covers an impressively broad lifecycle: workspace setup, workload configuration, planning, execution, extraction, analysis, artifact pipelines, inference, experiments, fault injection, comparisons, hypotheses, and findings. Minor gaps exist around generic resource removal/update operations, but the domain is largely immutable by design and workflows generally have no dead ends.

    Maintenance

    ActivityActive
    ResponsivenessResponsive

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