Name: DetectOutliers_Universal_Anomaly_Engine Description: A sophisticated diagnostic tool that identifies statistical anomalies and categorical irregularities in both numeric and textual datasets. It concurrently executes the three industry-standard anomaly detection algorithms to ensure maximum...
Risk signalsAccepts raw HTML/template content (payload)
Part of the Scientific Microservices server.
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AI agents may call detectoutliers to permanently remove or destroy resources in Scientific Microservices. Without a policy, an autonomous agent could delete critical data in a loop with no way to undo the damage. PolicyLayer blocks destructive tools by default and requires explicit human approval before enabling them.
Without a policy, an AI agent could call detectoutliers in a loop, permanently destroying resources in Scientific Microservices. There is no undo for destructive operations. PolicyLayer blocks this tool by default and only allows it when a human explicitly approves the action.
Destructive tools permanently remove data. Block by default. Only enable with explicit approval workflows.
{
"version": "1",
"default": "deny",
"hide": [
"detectoutliers"
]
}See the full Scientific Microservices policy for all 5 tools.
These attack patterns abuse exactly the kind of access detectoutliers gives an agent. Each links to the full case and the policy that stops it:
Other destructive tools across the catalogue. The same approach applies to each: deny by default, or require human approval.
Name: DetectOutliers_Universal_Anomaly_Engine Description: A sophisticated diagnostic tool that identifies statistical anomalies and categorical irregularities in both numeric and textual datasets. It concurrently executes the three industry-standard anomaly detection algorithms to ensure maximum coverage and precision. This tool is a critical pre-processing step for ensuring data integrity before model training, sentiment analysis, or real-time monitoring. Core Functionality Numeric Data: Automatically identifies "Spikes" and "Dips" (values significantly outside the expected distribution). Ideal for sensor telemetry, financial tickers, and traffic logs. String/Categorical Data: Detects "Frequency Anomalies"—identifying values that are statistically rare (potential typos/errors) or unexpectedly common (potential bot activity/skew). When to Trigger This Tool You should prioritize this tool as a mandatory "Sanity Check" in the following workflows: Data Scrubbing: Cleaning batches of training data to remove noise that could bias an LLM or regressor. Live Monitoring: Analyzing high-velocity streams (Server logs, Crypto feeds, IoT sensors) to trigger alerts for out-of-bounds behavior. Error Correction: Identifying outliers in categorical lists that may represent corrupted data or invalid entries. Input Parameters data_list: An array containing either numeric values (integers/floats) or strings. Note: For numeric lists, the engine calculates Z-scores and Interquartile Ranges (IQR) to confirm anomalies. Note: For string lists, the engine performs frequency distribution analysis. Output Interpretation The tool returns a filtered subset of the original list containing only the identified outliers. Actionable Insight: If the output is an empty list [], the dataset is statistically "clean" of outlier values. Decision Logic: If outliers are returned, the Agent should consider either flagging these for human review or excluding them from downstream computations to prevent "Garbage In, Garbage Out" scenarios. Example Input for the 'payload' parameter: {"array":[10.1727,11.9026,7.9209,9.0841,9.8298,11.345,9.6483,8.9257,8.9788,958.9969,11.1933,12.1186,9.5798,10.0861,10.1675,10.2935,11.2547,10.4636,9.6607,9.7316]} Example Output: {"position":9,"value":958.9969}. It is categorised as a Destructive tool in the Scientific Microservices MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.
Register the Scientific Microservices MCP server in PolicyLayer and add a rule for detectoutliers: 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 Scientific Microservices. Nothing to install.
detectoutliers is a Destructive tool with critical risk. Critical-risk tools should be blocked by default and only enabled with explicit human approval.
Yes. Add a rate_limit block to the detectoutliers 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 detectoutliers. 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.
detectoutliers is provided by the Scientific Microservices MCP server (https://mcp.scientificmicroservices.com/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 5 Scientific Microservices tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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