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klei30

Tuner MCP

by klei30
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Python
Hybrid

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    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

    C2.8/5.0

    Scored across 48 tools

    Disambiguation3/5

    Most tools are grouped by resource and action, but the large set has several close planning/staging tools (experiment_autoplan/training_plan/recipe_plan, dataset_fetch_hf/dataset_probe_hf/dataset_prepare, dataset_validate/dataset_inspect) that could cause misselection. Descriptions help, but boundaries are not always obvious.

    Naming Consistency3/5

    Names consistently use snake_case and resource prefixes, but action position varies: list/get tools are noun_verb (recipes_list, checkpoint_get) while training actions are verb_noun (train_sft, compute_logprobs) and some are bare verbs (evaluate, sample). The prefix grouping keeps it readable, but the mixed conventions are not fully predictable.

    Tool Count2/5

    48 tools is far beyond the 25+ threshold and would overwhelm an agent even though the domain spans training, datasets, checkpoints, and evals. Several narrow operations (dataset_validate vs dataset_inspect, checkpoint_* variants) could be consolidated.

    Completeness4/5

    The surface covers the full training lifecycle: datasets, recipes, plan/start/resume/stop training, multiple RL/DPO/SFT/distill methods, checkpoint management, evaluation, sessions, and usage. Minor gaps exist (no evaluation_list, no training/cancel/delete for remote runs, no dataset deletion), but agents can complete core workflows.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources