Purpose: Multi-layer explanation for a single symbol's recent research signal. Combines (1) technical score_trace from the signals store, (2) Thompson + regime scores from the virtual decision log, (3) news causality context. Use this when an AI must present a structured "why" rather than a raw v...
Part of the OneQAZ Trading Intelligence server.
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AI agents call explain_decision to retrieve information from OneQAZ Trading Intelligence 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 explain_decision 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": {
"explain_decision": {}
}
}See the full OneQAZ Trading Intelligence policy for all 32 tools.
These attack patterns abuse exactly the kind of access explain_decision 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.
Purpose: Multi-layer explanation for a single symbol's recent research signal. Combines (1) technical score_trace from the signals store, (2) Thompson + regime scores from the virtual decision log, (3) news causality context. Use this when an AI must present a structured "why" rather than a raw verdict. When to call: when the user asks "why is this signal bullish/bearish?". Prerequisites: identify the symbol via get_signals or get_latest_decisions first. Next steps: none (this completes the explanation chain). Caveats: symbol must match the per-symbol signal store filename (lowercase). Output is research evidence, NOT a buy or sell recommendation. Args: market_id: Market identifier (crypto, kr_stock, us_stock; aliases coin/kr/us) symbol: Symbol to explain (e.g., btc, eth, 005930) Disclaimer: Information only, not investment advice.. It is categorised as a Read tool in the OneQAZ Trading Intelligence MCP Server, which means it retrieves data without modifying state.
Register the OneQAZ Trading Intelligence MCP server in PolicyLayer and add a rule for explain_decision: 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 OneQAZ Trading Intelligence. Nothing to install.
explain_decision 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 explain_decision 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 explain_decision. 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.
explain_decision is provided by the OneQAZ Trading Intelligence MCP server (pypi:oneqaz-trading-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 32 OneQAZ Trading Intelligence tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
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