Scout Monitoring MCP

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get_job_metrics

Fetch a single timeseries metric—throughput, execution time, latency, errors, or allocations—for a specific background job and time range to diagnose performance issues.

Instructions

Get a single timeseries metric for a specific background job in an application.

Valid job metrics: throughput, execution_time, latency, errors, allocations.

Args: app_id (int): The ID of the Scout APM application. job_id (str): The Base64-encoded job ID. metric (str): The metric to retrieve (e.g., "execution_time", "throughput"). from_ (str): The start datetime in ISO 8601 format. to (str): The end datetime in ISO 8601 format.

Input Schema

Name	Required	Description	Default
`to`	Yes
`from_`	Yes
`app_id`	Yes
`job_id`	Yes
`metric`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Addedv1.0.1May 5, 2026

TDQS

A4.1/5.0

Behavior3/5

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

No annotations are provided, so the description must carry the full burden. It does supply useful behavioral context: a list of valid metrics, Base64 encoding for job_id, and ISO 8601 datetime format. However, it omits key behaviors such as read-only nature, error handling, or permissions.

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 front-loaded with a clear purpose sentence, followed by a concise list of valid metrics, and then a structured Args section. Every sentence adds value with no redundancy.

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?

Given the complexity (5 required parameters, no annotations, no schema descriptions) and the presence of an output schema, the description provides enough detail for correct invocation. It could be more complete with notes on error handling or metric case sensitivity, but overall it is sufficient.

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%, and the description fully compensates by describing each parameter: app_id type and meaning, job_id encoding, metric examples, and datetime formats for from_ and to. This adds significant meaning beyond the schema's bare titles.

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 states a specific verb ('Get') and resource ('timeseries metric') scoped to a specific background job in an application. It implicitly distinguishes from app-level and endpoint-level metric tools, making the purpose clear.

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?

Usage is implied by the phrase 'for a specific background job', but the description does not explicitly state when to use this tool versus siblings like get_app_metrics or get_endpoint_metrics. No alternatives or exclusions are mentioned.

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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