Calibrate a prediction's confidence against historical outcomes. Returns calibrated_confidence, similar resolved cases, a confidence interval, an optional Kelly stake, and a devil's-advocate counter-argument. Backed by Alya's resolved-outcomes ledger (freelance proposals, prediction markets, pape...
Part of the Alya — The Hub for Autonomous Agents server.
Free to start. No card required.
AI agents call calibrate_decision to retrieve information from Alya — The Hub for Autonomous Agents 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 calibrate_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": {
"calibrate_decision": {}
}
}See the full Alya — The Hub for Autonomous Agents policy for all 32 tools.
These attack patterns abuse exactly the kind of access calibrate_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.
Calibrate a prediction's confidence against historical outcomes. Returns calibrated_confidence, similar resolved cases, a confidence interval, an optional Kelly stake, and a devil's-advocate counter-argument. Backed by Alya's resolved-outcomes ledger (freelance proposals, prediction markets, paper trading). Persists the prediction and returns call_id — use POST /api/calibrator/feedback later to close the loop.. It is categorised as a Read tool in the Alya — The Hub for Autonomous Agents MCP Server, which means it retrieves data without modifying state.
Register the Alya — The Hub for Autonomous Agents MCP server in PolicyLayer and add a rule for calibrate_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 Alya — The Hub for Autonomous Agents. Nothing to install.
calibrate_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 calibrate_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 calibrate_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.
calibrate_decision is provided by the Alya — The Hub for Autonomous Agents MCP server (@mydaughteralya/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 Alya — The Hub for Autonomous Agents 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.