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scoutapp

Scout Monitoring MCP

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get_app_jobs

Retrieve all background jobs for an application along with aggregated performance metrics to identify high throughput, latency, or error rate jobs within a specified time range.

Instructions

Get all background jobs for a specific application. Also gets aggregated performance
metrics within the window of "from_" to "to". Useful for identifying high
throughput, high latency or high error rate jobs across the application with a
single call.

Each job includes: full_name, name, queue, job_id, throughput, execution_time,
time_consumed, latency.

Args:
    app_id (int): The ID of the Scout APM application.
    from_ (str): The start datetime in ISO 8601 format.
    to (str): The end datetime in ISO 8601 format.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
app_idYes
from_Yes
toYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It clearly describes the tool as a read operation returning job details and metrics, but it doesn't explicitly state it is read-only or mention behavior on invalid dates. The field list adds transparency.

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 concise and well-structured: a brief purpose and use case, a list of returned fields, and a clear parameter list. No unnecessary information.

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?

Despite having an output schema, the description lists key fields. It covers purpose, parameters, return content, and a use case. For a simple listing tool, it is complete and informative.

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?

The input schema has 0% description coverage, but the description fully explains each parameter: app_id is the application ID, from_ and to are start/end datetimes in ISO 8601 format. This adds essential meaning beyond the 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?

The description clearly states it gets all background jobs for a specific application and also aggregated performance metrics. It names specific fields and use case, making the tool's purpose unambiguous and distinct from siblings like get_app_job_traces.

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

Usage Guidelines4/5

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

The description explains the tool is useful for identifying high throughput, latency, or error rate jobs with a single call, providing clear usage context. However, it does not explicitly mention when not to use it or compare to alternatives like get_app_job_traces.

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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