Store a trade into OWM multi-layer memory with automatic updates. Writes to episodic memory and automatically updates semantic (Bayesian), procedural (running averages), and affective (EWMA confidence/streaks). Also writes to trade_records for backward compatibility. Args: symbol: Trading instrum...
Risk signalsHigh parameter count (15 properties)
Part of the Pypi:tradememory Protocol server.
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AI agents call remember_trade to retrieve information from Pypi:tradememory Protocol without modifying any data. This is common in research, monitoring, and reporting workflows where the agent needs context before taking action. Because read operations don't change state, they are generally safe to allow without restrictions -- but you may still want rate limits to control API costs.
Even though remember_trade only reads data, uncontrolled read access can leak sensitive information or rack up API costs. An agent caught in a retry loop could make thousands of calls per minute. A rate limit gives you a safety net without blocking legitimate use.
Read-only tools are safe to allow by default. No rate limit needed unless you want to control costs.
{
"version": "1",
"default": "deny",
"tools": {
"remember_trade": {}
}
}See the full Pypi:tradememory Protocol policy for all 15 tools.
These attack patterns abuse exactly the kind of access remember_trade gives an agent. Each links to the full case and the policy that stops it:
Other read tools across the catalogue. The same approach applies to each: allow, with a rate cap to control cost.
Store a trade into OWM multi-layer memory with automatic updates. Writes to episodic memory and automatically updates semantic (Bayesian), procedural (running averages), and affective (EWMA confidence/streaks). Also writes to trade_records for backward compatibility. Args: symbol: Trading instrument (e.g. "XAUUSD") direction: "long" or "short" entry_price: Entry price of the trade exit_price: Exit price of the trade pnl: Profit/loss in account currency strategy_name: Strategy used (e.g. "VolBreakout") market_context: Description of market conditions pnl_r: P&L as R-multiple (risk units). Improves OWM scoring quality. context_regime: Market regime (trending_up/trending_down/ranging/volatile) context_atr_d1: ATR(14) on D1 in dollars confidence: Agent confidence level 0-1 (default 0.5) reflection: Lessons learned from this trade max_adverse_excursion: Maximum adverse excursion during the trade trade_id: Optional custom ID. Auto-generated if omitted. timestamp: ISO format timestamp. Defaults to now (UTC).. It is categorised as a Read tool in the Pypi:tradememory Protocol MCP Server, which means it retrieves data without modifying state.
Register the Pypi:tradememory Protocol MCP server in PolicyLayer and add a rule for remember_trade: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Pypi:tradememory Protocol. Nothing to install.
remember_trade is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the remember_trade rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for remember_trade. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
remember_trade is provided by the Pypi:tradememory Protocol MCP server (pypi:tradememory-protocol). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 15 Pypi:tradememory Protocol tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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