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pranav797

Credit Risk MCP Server

by pranav797
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Python
Hybrid

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    TDQS

    A4.5/5.0

    Scored across 5 tools

    Disambiguation5/5

    Each tool targets a distinct purpose: score_borrower (single), compare_borrowers (batch ranking), explain_prediction (local SHAP), get_feature_importance (global SHAP), and get_model_info (metadata). The descriptions explicitly contrast overlapping-sounding pairs like global feature importance vs per-borrower explanation, leaving no realistic selection ambiguity.

    Naming Consistency5/5

    All five names follow a clean verb_noun snake_case pattern (get_feature_importance, compare_borrowers, get_model_info, score_borrower, explain_prediction). The verbs are apt and used consistently with no mixing of conventions.

    Tool Count5/5

    Five tools is a well-scoped set for a model-inference domain, with each tool earning a distinct role (score, compare, explain, global importance, metadata). Nothing is redundant or missing for the apparent purpose.

    Completeness5/5

    The surface fully covers the inference lifecycle: single scoring, batch comparison, per-prediction explanation, global feature importance, and model metadata/limitations. As a stateless scoring service there are no CRUD gaps, and the tools form a coherent complete workflow.

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