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ThoTischner

observability-mcp

detect_anomalies

Scan monitored services for abnormal behavior and receive a severity-ranked list of anomalies, enabling quick triage of potential issues.

Instructions

Scan one or all monitored services for abnormal behavior and return the findings ranked by severity. When to use: the entry point for 'is anything wrong anywhere?' triage. Once a service is flagged, follow up with get_service_health for the verdict or query_metrics/query_logs for the raw evidence. Behavior: read-only, no side effects. Applies z-score analysis to metrics, detects log error-rate spikes, and correlates the two. Returns a list of anomalies, each with the affected service, metric/signal, severity, the deviation (e.g. σ and % change), and a short explanation. No anomalies yields an empty list, not an error. Related: get_service_health (single-service verdict), query_metrics (raw series behind a flagged metric).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
serviceNoOptional. Restrict the scan to one service (exact, case-sensitive name from `list_services`). Default: scan every monitored service.
durationNoOptional. Look-back window analyzed for anomalies, written as <number><unit> with unit s|m|h|d (e.g. '5m', '15m', '1h'). Default: '10m'.
sensitivityNoOptional. Detection threshold: 'low' flags only strong deviations (>3σ), 'medium' is balanced (>2σ), 'high' is most sensitive and noisier (>1.5σ). Default: 'medium'.
Behavior5/5

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

With no annotations, the description fully discloses read-only behavior, no side effects, the analytical methods (z-score, log spikes, correlation), and return format including empty list edge case.

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

Conciseness4/5

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

Well-structured with clear paragraphs for purpose, usage, behavior, return, and related tools. Slightly verbose but every sentence adds value; front-loaded with the key purpose.

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?

No output schema, yet description fully explains return value fields and edge case. Covers all parameters, references sibling tools, and provides enough context for effective tool invocation.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3. The description adds extra meaning: explains default behavior for service (scan all), duration (10m), and provides detailed explanation of sensitivity thresholds beyond the schema's enum values.

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 the tool scans services for anomalies and returns ranked findings. It distinguishes from siblings like get_service_health and query_metrics by positioning itself as the triage entry point.

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?

Explicitly states 'when to use' as the triage entry point and provides follow-up steps with specific sibling tool names. Offers clear guidance on next actions after receiving results.

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

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