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EvalForge Lite

by thejaredchapman
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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

    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

    ActivityMaintained
    ResponsivenessNo issues

    Resources