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llmledger

llmledger

by pouriaetab
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

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    Deploy Server
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    TDQS

    A4.4/5.0

    Scored across 11 tools

    Disambiguation5/5

    Each tool has a distinct purpose and input context, and the descriptions explicitly exclude neighboring tools. Cost/time estimators may look similar at a glance, but they are cleanly separated by data source: workload, promptfoo sample, logged runs, usage export, or local hardware.

    Naming Consistency4/5

    All names use consistent snake_case and most follow a clear verb_noun pattern, such as find_models, compare_models, estimate_cost, and log_run. A few names are noun or adjective phrases, like price_history and not_yet_priced, but they remain readable and predictable.

    Tool Count5/5

    Eleven tools is well-scoped for an LLM pricing, comparison, and forecasting server. Each tool covers a meaningful workflow rather than duplicating another, so the set does not feel bloated or thin.

    Completeness5/5

    The surface covers model discovery, direct comparison, cost and time estimation, historical pricing, future/unpriced models, prompt-based recommendation, usage repricing, local-vs-cloud analysis, and run logging. These tools form a complete lifecycle for the stated domain with no obvious dead ends.

    Maintenance

    ActivityMaintained
    ResponsivenessLimited data

    Resources