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. Args: symbol: Trading instrument (e.g. "XAUUSD") market_cont...
Part of the Pypi:tradememory Protocol server.
Free to start. No card required.
AI agents call recall_memories 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 recall_memories 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": {
"recall_memories": {}
}
}See the full Pypi:tradememory Protocol policy for all 15 tools.
These attack patterns abuse exactly the kind of access recall_memories 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.
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. Args: symbol: Trading instrument (e.g. "XAUUSD") market_context: Current market conditions to match against context_regime: Current market regime (trending_up/trending_down/ranging/volatile) context_atr_d1: Current ATR(14) on D1 in dollars strategy_name: Optional strategy filter memory_types: Types to query (default: ["episodic", "semantic"]) limit: Max results (default 10). 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 recall_memories: 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.
recall_memories 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 recall_memories 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 recall_memories. 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.
recall_memories 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.