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AiGanak LLM Cost

AiGanak LLM Cost

by AiGanak
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Python
Remote

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    TDQS

    A3.5/5.0

    Scored across 7 tools

    Disambiguation4/5

    Most tools target a clearly distinct query: listing models, fetching one model's price, comparing across models, logging price changes, and measuring token inflation by language. The only real friction is between estimate_cost and estimate_text_cost, which both answer 'what will this cost' but differ in input shape (N requests vs. raw text); the descriptions disambiguate, but an agent could still pick the wrong one initially.

    Naming Consistency4/5

    Six of seven tools follow a clean snake_case verb_noun or verb_noun_phrase pattern (list_models, get_model_price, estimate_cost, compare_models, estimate_text_cost). The exceptions are price_changes and token_tax, which are noun-only names that break the verb-first convention, though both remain readable and descriptive.

    Tool Count5/5

    Seven tools is well-scoped for a pricing/cost-estimation server: each covers a distinct facet (catalog, single-model detail, single-model costing, cross-model comparison, token inflation, price history, text-based costing). No tool feels redundant or like padding.

    Completeness4/5

    The surface covers the full cost-analysis lifecycle: discover models, inspect prices, estimate costs, compare alternatives, account for language token overhead, and track price changes. Minor gaps exist, such as no aggregate/multi-model workload costing or budget-threshold tooling, but agents can compose existing tools to work around these.

    Maintenance

    ActivityMaintained
    ResponsivenessNo recent community issues

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