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llmledger

by pouriaetab
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    Shareable, User-Owned Context BackendPeople, applications, and agents read, write, and coordinate together in one context lake. Reach it over MCP, a CLI, a Python SDK, or REST.fulcradynamics.comAd

    TDQS

    B3.4/5.0

    Scored across 10 tools

    Disambiguation3/5

    Several tools overlap in purpose: find_models, compare_models, and suggest_for_project all recommend models; estimate_cost, forecast_job, and local_or_cloud all report costs from different angles; estimate_time and forecast_job both cover timing. Descriptions help clarify boundaries, but an agent could still misselect among them.

    Naming Consistency3/5

    All names use snake_case, but conventions are mixed: most are verb_noun (find_models, estimate_cost), while others are noun phrases or adjectives (price_history, not_yet_priced, local_or_cloud). The set is readable but does not follow a predictable verb-first pattern.

    Tool Count5/5

    With 10 tools, the set is well within the ideal 3-15 range for a specialized LLM pricing and forecasting server. Each tool addresses a distinct facet of the domain, and the count does not feel bloated or thin.

    Completeness4/5

    The surface covers model discovery, comparison, cost/time estimation, price history, predictive signals, project suggestions, run logging, job forecasting, and local-vs-cloud analysis. Minor gaps exist, such as direct model lookup by name or a full catalogue enumeration, but core workflows are covered.

    Maintenance

    ActivityMaintained
    ResponsivenessResponsive

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