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mnemox-ai

TradeMemory Protocol

by mnemox-ai

recall_memories

Idempotent

Recall outcome-weighted trade memories to avoid repeating past mistakes and adapt strategies to current market conditions.

Instructions

Recall memories using OWM outcome-weighted scoring.

Queries episodic and semantic memories, scores them by outcome quality, context similarity, recency, confidence, and affective modulation. Returns ranked memories with score breakdown.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results (default 10)
symbolYesTrading instrument (e.g. "XAUUSD")
use_hybridNoIf True (default), enable vector + OWM hybrid scoring when an embedding backend is available. Falls back to pure OWM silently when sentence-transformers is not installed.
hybrid_alphaNoVector vs OWM blend weight [0..1] when hybrid is active. 0.0 = pure OWM, 1.0 = pure vector. Default 0.3 (OWM-dominant).
memory_typesNoTypes to query (default: ["episodic", "semantic"])
strategy_nameNoOptional strategy filter
context_atr_d1NoCurrent ATR(14) on D1 in dollars
context_regimeNoCurrent market regime (trending_up/trending_down/ranging/volatile)
market_contextYesCurrent market conditions to match against

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description says 'Queries' and 'Recall', implying a read-only operation, while annotations declare readOnlyHint=false. This is a contradiction. The description also doesn't disclose any side effects (e.g., updating recency scores) or the silent fallback from hybrid to pure OWM, which is only documented in the schema parameter.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, with a clear first sentence and a short paragraph giving relevant detail. Every sentence adds value, with no fluff or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 9 parameters and an output schema, the description covers the main purpose, scoring factors, and return format. It lacks usage guidance and doesn't address the discrepancy with readOnlyHint=false, but the output schema presumably covers return structure, so overall it is mostly complete with a notable gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, with detailed parameter descriptions including defaults and fallback behavior for use_hybrid. The description adds scoring dimensions (outcome quality, context similarity, recency, confidence, affective modulation), but these are not parameters; it doesn't provide additional meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool recalls memories using OWM outcome-weighted scoring, and specifies it queries episodic and semantic memories, returns ranked memories with score breakdown. This distinguishes it from sibling tools like remember_trade (write operation) and get_trade_reflection (single trade reflection).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the tool is for retrieving memories, but does not explicitly state when to use it versus alternatives such as get_trade_reflection or get_behavioral_analysis. No exclusions or alternative tool references are provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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