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scoutapp

Scout Monitoring MCP

Official
by scoutapp

get_app_anomaly_events

List Scout APM anomaly events within a timeframe to find statistically significant metric deviations. Filter by state, metric, or endpoint to investigate response time spikes or throughput drops.

Instructions

List anomaly events for a Scout APM application within a timeframe.

Anomaly events flag statistically significant deviations in a metric (e.g.
response time spike, throughput drop) detected against a learned baseline.

Args:
    app_id: The ID of the Scout APM application.
    from_: Start datetime in ISO 8601 format.
    to: End datetime in ISO 8601 format.
    state: Filter by state - "open", "closed", or "all".
    metric: Filter by metric (e.g. response_time, throughput, error_rate).
    endpoint: Filter by endpoint (controller path).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toYes
from_Yes
stateNo
app_idYes
metricNo
endpointNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv2026.9.28

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. 'List' plus the filter semantics make the read-only nature reasonably clear, and the explanation of what anomaly events represent is useful domain context, but it discloses nothing about result limits, pagination, ordering, or default behavior for the optional filters.

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?

Purpose is front-loaded in the first sentence, followed by one line of useful domain context, then a compact Args list. It is efficient and well-ordered, with only mild redundancy between the header sentence and the Args block.

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

Completeness4/5

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

An output schema exists so return values need not be described, and every parameter is explained. The remaining gap is behavioral: defaults for the optional filters (e.g., what state defaults to) and any pagination/result-cap expectations are unstated.

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

Parameters5/5

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

Schema description coverage is 0%, yet the Args block documents all six parameters: app_id's meaning, ISO 8601 format for from_/to, the state enum values (open/closed/all), metric examples (response_time, throughput, error_rate), and endpoint as controller path. This fully compensates for the bare schema.

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?

States a specific verb (List) plus resource (anomaly events) plus scope (for a Scout APM application within a timeframe), and the singular sibling get_app_anomaly_event makes the plural/listing distinction clear. An agent can tell this apart from the other app-metric siblings without opening a schema.

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

Usage Guidelines3/5

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

The description explains what an anomaly event is (statistical deviation from a learned baseline) which implies when the tool is relevant, but it never states when to prefer this over get_app_metrics, get_app_anomaly_event, or get_app_insights, nor any prerequisites. Usage is only implied.

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