Low Risk

compare_anomaly

Compare one of your decisions against your accumulated pattern. Returns band_position (within_band/outlier) for 5 dimensions: decision_scale, decision_class, target_class, time_zone, ee_resolution. Costs DAC.

Part of the Decision Anchor server.

compare_anomaly is read-only, but an agent in a loop can still rack up calls and cost. PolicyLayer caps every call before it runs. Live in minutes.

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AI agents call compare_anomaly to retrieve information from Decision Anchor 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 compare_anomaly 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.

policy.json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "compare_anomaly": {}
  }
}

See the full Decision Anchor policy for all 24 tools.

Get this rule live on your own Decision Anchor server in minutes. PolicyLayer enforces it on every call, before it runs.

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These attack patterns abuse exactly the kind of access compare_anomaly gives an agent. Each links to the full case and the policy that stops it:

Browse the full MCP Attack Database →

Every attack above starts with a tool call. PolicyLayer checks each one against your policy first, so compare_anomaly only ever does what you allow.

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Other read tools across the catalogue. The same approach applies to each: allow, with a rate cap to control cost.

What does the compare_anomaly tool do? +

Compare one of your decisions against your accumulated pattern. Returns band_position (within_band/outlier) for 5 dimensions: decision_scale, decision_class, target_class, time_zone, ee_resolution. Costs DAC.. It is categorised as a Read tool in the Decision Anchor MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on compare_anomaly? +

Register the Decision Anchor MCP server in PolicyLayer and add a rule for compare_anomaly: 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 Decision Anchor. Nothing to install.

What risk level is compare_anomaly? +

compare_anomaly is a Read tool with low risk. Read-only tools are generally safe to allow by default.

Can I rate-limit compare_anomaly? +

Yes. Add a rate_limit block to the compare_anomaly 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.

How do I block compare_anomaly completely? +

Set action: deny in the PolicyLayer policy for compare_anomaly. 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.

What MCP server provides compare_anomaly? +

compare_anomaly is provided by the Decision Anchor MCP server (https://mcp.decision-anchor.com/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Decision Anchor tool call.

Deterministic rules across all 24 Decision Anchor tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.

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