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EvalForge Lite

by thejaredchapman
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Python
Hybrid

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    TDQS

    A3.7/5.0

    Scored across 9 tools

    Disambiguation4/5

    Most tools have clearly distinct purposes, but the three model-introspection tools (list_models, suggest_models, list_availability) overlap somewhat, and evaluate_prompt vs run_comparison could be momentarily confused since both invoke LLMs. Descriptions do help differentiate them, especially the explicit pre-run vs full-run distinction.

    Naming Consistency5/5

    All tools use snake_case with a consistent verb_noun pattern (list_models, suggest_models, set_policy, evaluate_prompt, run_comparison, list_runs, get_report). Even the format variant get_report_csv follows the same convention cleanly.

    Tool Count5/5

    Nine tools is well-scoped for a model-evaluation server, with each tool earning its place across catalog introspection, policy, evaluation, execution, and reporting. No padding or redundancy in count.

    Completeness4/5

    Coverage spans discovery, policy, prompt feedback, comparison runs, and report exports (PDF/CSV), which is solid for the domain. Minor gaps: no structured JSON run-detail retrieval or run/policy deletion, and set_policy has no corresponding get_policy to inspect current state.

    Maintenance

    ActivityActive
    ResponsivenessNo issues

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