Store a trade decision with full context into memory. Call this after executing a trade to build your memory bank. Include market_context and reflection for better recall later. Args: symbol: Trading instrument (e.g. "XAUUSD") direction: "long" or "short" entry_price: Entry price of the trade str...
Risk signalsHigh parameter count (10 properties)
Part of the Pypi:tradememory Protocol server.
Free to start. No card required.
AI agents invoke store_trade_memory to trigger processes or run actions in Pypi:tradememory Protocol. Execute operations can have side effects beyond the immediate call -- triggering builds, sending notifications, or starting workflows. Rate limits and argument validation are essential to prevent runaway execution.
store_trade_memory can trigger processes with real-world consequences. An uncontrolled agent might start dozens of builds, send mass notifications, or kick off expensive compute jobs. PolicyLayer enforces rate limits and validates arguments to keep execution within safe bounds.
Execute tools trigger processes. Rate-limit and validate arguments to prevent unintended side effects.
{
"version": "1",
"default": "deny",
"tools": {
"store_trade_memory": {
"limits": [
{
"counter": "store_trade_memory_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}See the full Pypi:tradememory Protocol policy for all 15 tools.
These attack patterns abuse exactly the kind of access store_trade_memory gives an agent. Each links to the full case and the policy that stops it:
Other execute tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
Store a trade decision with full context into memory. Call this after executing a trade to build your memory bank. Include market_context and reflection for better recall later. Args: symbol: Trading instrument (e.g. "XAUUSD") direction: "long" or "short" entry_price: Entry price of the trade strategy_name: Name of the strategy used (e.g. "VolBreakout") market_context: Description of market conditions when trade was taken exit_price: Exit price (if trade is closed) pnl: Profit/loss in account currency (if trade is closed) reflection: What you learned from this trade trade_id: Optional custom ID. Auto-generated if omitted. timestamp: ISO format timestamp. Defaults to now (UTC).. It is categorised as a Execute tool in the Pypi:tradememory Protocol MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Pypi:tradememory Protocol MCP server in PolicyLayer and add a rule for store_trade_memory: 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.
store_trade_memory is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the store_trade_memory 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 store_trade_memory. 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.
store_trade_memory 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.
Free to start. No card required.
4,600+ MCP servers and 31,000+ tools scanned and risk-classified.