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create_pa_indicator

Define a Performance Analytics indicator (KPI) on pa_indicators by setting the source facts table, aggregation, and conditions. Data is collected later via a PA job.

Instructions

Create a Performance Analytics (PA) indicator / KPI on pa_indicators (requires WRITE_ENABLED=true). Define the source facts table, aggregation and conditions; collect data via a PA job afterward.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesIndicator name (e.g. "Open P1 incidents")
unitNoUnit (pa_units name or sys_id)
fieldNoField to aggregate (required for sum/average/max/min; ignored for count)
activeNoActivate immediately (default: true)
aggregateNoAggregation function
directionNoDesired trend
conditionsNoEncoded query on the facts table (e.g. "active=true^priority=1")
descriptionNoWhat the indicator measures
facts_tableNoSource/facts table the indicator counts (e.g. "incident")
Behavior4/5

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

Beyond the annotations (readOnlyHint=false, etc.), it discloses the WRITE_ENABLED requirement and the need to run a PA job afterward, which clarifies the indicator isn't immediately populated. It doesn't cover return values, but gives useful operational context.

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?

Two concise sentences deliver purpose, prerequisite, key configuration, and a follow-up step. No filler; minimal and effective.

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 no output schema, the description doesn't explain what the API returns, but it covers the creation flow and post-creation step (PA job). It could mention the response format, but the information provided is sufficient for core usage.

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?

Schema coverage is 100%, so all parameters are documented in the schema. The description adds only a high-level summary ('facts table, aggregation and conditions') without new parameter-level details, so baseline 3 is appropriate.

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?

Clearly states it creates a PA indicator/KPI on the pa_indicators table, with a specific verb and resource. It also differentiates from sibling tools like list_pa_indicators and create_pa_breakdown by naming the target table and core configuration fields.

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?

Provides context that it's for creating indicators, and hints at the follow-up action ('collect data via a PA job afterward'). Also gives a prerequisite (WRITE_ENABLED=true). However, it doesn't explicitly name alternatives or exclusions, so a 5 isn't warranted.

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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