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TradeMemory Protocol

TradeMemory Protocol

by mnemox-ai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

compute_dqs

Idempotent

Score a trade's decision quality before execution by checking regime match, position sizing versus Kelly, process adherence, risk state, and historical pattern.

Instructions

Compute Decision Quality Score before executing a trade.

Evaluates the quality of the decision process (not outcome) across 5 factors: regime match, position sizing vs Kelly, process adherence (OWM similarity), risk state, and historical pattern.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "symbol": "<symbol>",
      "strategy_name": "<strategy_name>",
      "direction": "<direction>"
    },
    "name": "compute_dqs"
  }
}

Input Schema

NameRequiredDescriptionDefault
symbolYesTrading instrument (e.g. "XAUUSD").
directionYesIntended direction ("long" or "short").
strategy_nameYesStrategy being considered (e.g. "VolBreakout").
context_atr_d1NoATR(14) on D1 in dollars.
context_regimeNoMarket regime (trending_up/trending_down/ranging/volatile).
market_contextNoDescription of current market conditions.
proposed_lot_sizeNoPlanned position size in lots (default 0.1).

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.5.4

TDQS

A4.5/5.0
Behavior4/5

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

The description adds valuable behavioral context beyond annotations by listing the five factors it evaluates (regime match, position sizing vs Kelly, OWM similarity, risk state, historical pattern). The annotations already declare idempotentHint=true and destructiveHint=false, so the description does not need to repeat safety aspects. No contradiction exists.

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 tightly written in two sentences. The first sentence front-loads the primary purpose, and the second lists the five evaluation factors without any filler. Every word earns its place.

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

Completeness5/5

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

Given the tool's complexity (7 parameters, output schema present, annotations provided), the description is complete. It explains what the tool does, when to use it, and what factors are considered. The output schema handles return-value details, so no further explanation is needed.

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

Parameters4/5

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

Schema coverage is 100%, giving the agent complete parameter documentation. The description goes a step further by grouping parameters conceptually: e.g., 'position sizing vs Kelly' connects to proposed_lot_size, and 'regime match' connects to context_regime. This semantic mapping adds value beyond the raw 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 opens with a specific verb+resource: 'Compute Decision Quality Score before executing a trade.' It clearly distinguishes this from sibling tools by emphasizing that it evaluates the decision process (not outcome) across five named factors, which differentiates it from tools like check_trade_legitimacy or validate_strategy.

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

Usage Guidelines4/5

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

The description clearly states when to use it: 'before executing a trade.' It provides strong contextual guidance, but it does not explicitly name alternatives or exclusion cases. Sibling tools exist for related purposes (e.g., check_trade_legitimacy, validate_strategy), but no comparison is given, so it falls just short of a 5.

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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