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TradeMemory Protocol

by mnemox-ai
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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

    A3.5/5.0

    Scored across 20 tools

    Disambiguation4/5

    Most tools have clearly distinct purposes across evolution, audit, and memory subsystems. However, check_trade_legitimacy, compute_dqs, and get_agent_state all involve pre-trade risk/confidence evaluation, creating potential overlap for an agent deciding which to call.

    Naming Consistency4/5

    All tool names use snake_case with a clear verb_noun structure, which is consistent overall. Minor deviation: evolution_* tools carry a subsystem prefix while other tools do not, but the pattern remains predictable.

    Tool Count3/5

    20 tools is on the heavy side for a single server, spanning multiple subsystems (evolution, audit, memory, planning). While each tool appears to have a distinct role, the count is borderline excessive and could likely be consolidated.

    Completeness4/5

    The surface covers major lifecycle stages: data fetch, pattern discovery, backtesting, evolution, audit export/verification, memory storage/recall, strategy validation, and trade planning. Minor gaps exist, such as no explicit cancel/update for trading plans or delete for memories, but core workflows are supported.

    Maintenance

    ActivityMaintained
    ResponsivenessSlow

    Resources