Update a prior probability with weighted evidence using a Beta-Bayesian posterior. Use for incremental belief revision: starting from a baseline probability, fold in new signals (each with a value in [0,1] and a weight) and get an updated posterior plus calibration score. Suited to fraud-risk sco...
Part of the Oraclaw server.
Free to start. No card required.
AI agents use predict_bayesian to create or modify resources in Oraclaw. Write operations carry medium risk because an autonomous agent could trigger bulk unintended modifications. Rate limits prevent a single agent session from making hundreds of changes in rapid succession. Argument validation ensures the agent passes expected values.
Without a policy, an AI agent could call predict_bayesian repeatedly, creating or modifying resources faster than any human could review. PolicyLayer's rate limiting ensures write operations happen at a controlled pace, and argument validation catches malformed or unexpected inputs before they reach Oraclaw.
Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.
{
"version": "1",
"default": "deny",
"tools": {
"predict_bayesian": {
"limits": [
{
"counter": "predict_bayesian_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}See the full Oraclaw policy for all 17 tools.
These attack patterns abuse exactly the kind of access predict_bayesian gives an agent. Each links to the full case and the policy that stops it:
Other write tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
Update a prior probability with weighted evidence using a Beta-Bayesian posterior. Use for incremental belief revision: starting from a baseline probability, fold in new signals (each with a value in [0,1] and a weight) and get an updated posterior plus calibration score. Suited to fraud-risk scoring, A/B test stopping decisions, diagnostic probability stacking. For combining N independent model predictions, use predict_ensemble. For full distribution sampling, use simulate_montecarlo. Free.. It is categorised as a Write tool in the Oraclaw MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Oraclaw MCP server in PolicyLayer and add a rule for predict_bayesian: 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 Oraclaw. Nothing to install.
predict_bayesian is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the predict_bayesian 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 predict_bayesian. 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.
predict_bayesian is provided by the Oraclaw MCP server (@oraclaw/mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 17 Oraclaw tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
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