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List anomaly detectors

malcolm_anomaly_detectors
Read-only

List OpenSearch anomaly detectors to review machine-learning baselines and see any recorded anomalies across all detectors.

Instructions

List OpenSearch anomaly detectors, what each models, and whether any anomalies exist.

    Use this to see what machine-learning baselines Malcolm is maintaining
    over the traffic and whether they have produced anything. It counts
    anomalies across every detector at once; for which entities one named
    detector scored, and when, take its `id` to malcolm_anomaly_results.
    This reads the detector configuration, not the traffic: for the
    underlying documents use malcolm_search, and for Suricata's
    signature-based alerts use malcolm_alerts, which is a different
    detection method entirely.

    Returns JSON {"total", "showing", "recorded_anomalies", "detectors"};
    per-detector fields are in the output schema, minus the aggregation
    definitions behind each feature, which are configuration detail.
    `recorded_anomalies` counts anomalous results across all detectors, NOT
    detector runs. Zero with detectors configured still needs care: a
    detector that was never started produces the same zero.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax detectors to return.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.0.2

TDQS

A4.7/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the readOnlyHint and destructiveHint annotations, the description adds critical context: it reads detector configuration rather than traffic, counts anomalies across detectors (not detector runs), and warns that zero recorded_anomalies can mean a detector never started. This proactively addresses interpretation pitfalls.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is multi-sentence but every sentence adds distinct value: purpose, usage context, sibling differentiation, output shape, and an important zero-value caveat. It is well-structured and front-loaded, with no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the output schema exists and the tool has a single well-documented parameter, the description covers all necessary context: what is returned, what is intentionally excluded, how it relates to sibling tools, and how to interpret edge-case results. Nothing critical is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema fully describes the single 'limit' parameter with a clear description ('Max detectors to return.'), so schema coverage is 100%. The description adds no additional parameter semantics, but the baseline of 3 applies because the schema already handles this.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states what the tool does: lists OpenSearch anomaly detectors, what each models, and whether anomalies exist. It distinguishes itself from siblings by explicitly naming malcolm_anomaly_results for per-detector results, malcolm_search for underlying documents, and malcolm_alerts for signature-based alerts.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit guidance is provided: use this to see machine-learning baselines and their anomaly outputs; when to use alternatives is clearly stated (e.g., take an id to malcolm_anomaly_results for entity-level scoring, use malcolm_search for traffic, use malcolm_alerts for Suricata alerts). This offers strong when/when-not guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.