TradeMemory Protocol

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Recall Memories

recall_memories

Idempotent

Retrieve ranked episodic and semantic trade memories by outcome-weighted scoring of quality, context similarity, recency, confidence, and affect to guide strategy adjustments and position sizing.

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

Name	Required	Description
`limit`	No	Max results (default 10)
`symbol`	Yes	Trading instrument (e.g. "XAUUSD")
`use_hybrid`	No	If 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_alpha`	No	Vector vs OWM blend weight [0..1] when hybrid is active. 0.0 = pure OWM, 1.0 = pure vector. Default 0.3 (OWM-dominant).
`memory_types`	No	Types to query (default: ["episodic", "semantic"])
`strategy_name`	No	Optional strategy filter
`context_atr_d1`	No	Current ATR(14) on D1 in dollars
`context_regime`	No	Current market regime (trending_up/trending_down/ranging/volatile)
`market_context`	Yes	Current market conditions to match against

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed9 schema fields changedv0.5.4Aug 2, 2026

addedInput schema / properties / context_atr_d1 / description
```
Added value: +"Current ATR(14) on D1 in dollars"
```

addedInput schema / properties / context_regime / description

Added value: +"Current market regime (trending_up/trending_down/ranging/volatile)"

addedInput schema / properties / hybrid_alpha

Added value: +{
+  "default": 0.3,
+  "description": "Vector vs OWM blend weight [0..1] when hybrid is active.\n0.0 = pure OWM, 1.0 = pure vector. Default 0.3 (OWM-dominant).",
+  "type": "number"
+}

addedInput schema / properties / limit / description
```
Added value: +"Max results (default 10)"
```

addedInput schema / properties / market_context / description

Added value: +"Current market conditions to match against"

addedInput schema / properties / memory_types / description

Added value: +"Types to query (default: [\"episodic\", \"semantic\"])"

addedInput schema / properties / strategy_name / description
```
Added value: +"Optional strategy filter"
```

addedInput schema / properties / symbol / description

Added value: +"Trading instrument (e.g. \"XAUUSD\")"

addedInput schema / properties / use_hybrid

Added value: +{
+  "default": true,
+  "description": "If True (default), enable vector + OWM hybrid scoring when\nan embedding backend is available. Falls back to pure OWM silently\nwhen sentence-transformers is not installed.",
+  "type": "boolean"
+}

First observedv0.4.0Mar 12, 2026

TDQS

B3.2/5.0

Behavior3/5

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

Annotations already declare idempotent=true, destructiveHint=false, and — somewhat surprisingly for a query tool — readOnlyHint=false. The description adds useful behavioral context by naming the scoring dimensions and stating that ranked results with score breakdown are returned. It does not address the readOnlyHint=false tension (e.g. whether recall mutates memory state/decay), so a moderate score is warranted.

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

Conciseness4/5

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

The description is three short, front-loaded sentences with no filler; the ranking factors and return shape are conveyed efficiently. It could be slightly more compact, but nothing is wasted.

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?

With an output schema present, the description needn't explain return values, and it still summarizes what is returned (ranked memories with score breakdown). Together with full parameter coverage it is largely complete, missing only when-to-use routing against siblings.

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 description coverage is 100% and all 9 parameters are documented in the schema, including hybrid_alpha, context_regime, and market_context. The description adds no parameter-level detail (e.g. the effect of limit or memory_types), so the baseline 3 applies.

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

Purpose4/5

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

The description states a specific verb (recall) and resource (episodic and semantic memories) and explains the OWM outcome-weighted scoring that distinguishes it from siblings like remember_trade. It's clear what the tool does, though it doesn't explicitly name the alternative retrieval/write tools it differs from.

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

Usage Guidelines2/5

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

No explicit when-to-use or when-not-to-use guidance is given, nor any mention of prerequisites or the sibling tools (e.g. remember_trade) that would be chosen instead. Usage is only implied by the word 'recall'.

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