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TradeMemory Protocol

by mnemox-ai
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Python
Hybrid

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    Context All Your Agents Can ShareAgents share what they learn, hand off work, and hold it when you switch models. Start with 5 GB free.fulcradynamics.comAd

    TDQS

    A4/5.0

    Scored across 20 tools

    Disambiguation4/5

    Most tools target clearly distinct resources and actions, and namespaces (evolution_*, verify_audit_*) help group related operations. However, check_trade_legitimacy vs compute_dqs (both pre-trade gates) and get_agent_state vs get_behavioral_analysis (both aggregate state) have overlapping boundaries an agent could misselect between.

    Naming Consistency4/5

    Nearly all tools follow a predictable snake_case verb_noun pattern (get_*, create_*, verify_*, check_*), with the evolution_* prefix used consistently as a namespace for that subgroup. Minor deviation is the mixed imperative/noun style of remember_trade, recall_memories, and compute_dqs, but the convention remains readable throughout.

    Tool Count4/5

    20 tools is on the heavier side but justified by the breadth of the domain (memory, planning, strategy evolution, audit, validation). Each tool maps to a distinct capability rather than being redundant, so the count is reasonable though slightly toward the upper bound.

    Completeness4/5

    Coverage is strong: memory write/recall, planning create/check, a full evolution loop, and a complete audit trail (export, hash, chain, Merkle root). The notable gap is the planning lifecycle—create_trading_plan mentions manual cancellation but no cancel/update plan tool exists, and there is no delete/update for stored trades.

    Maintenance

    ActivityMaintained
    ResponsivenessSlow

    Resources