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get_pa_time_series

Retrieve historical time-series data for Performance Analytics indicators to analyze trends and patterns over specified date ranges.

Instructions

Get historical time-series data for a PA indicator to identify trends

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
indicator_sys_idYesPA indicator sys_id
start_dateNoStart date in YYYY-MM-DD format (default: 30 days ago)
end_dateNoEnd date in YYYY-MM-DD format (default: today)
limitNoMax data points to return (default 100)
Behavior2/5

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

No annotations are provided, so the description carries full burden. It states 'Get historical time-series data', implying a read-only operation, but doesn't disclose behavioral traits like authentication needs, rate limits, pagination, error handling, or data format. For a tool with no annotations, this leaves significant gaps in understanding how it behaves.

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?

The description is a single, efficient sentence that states the core purpose and goal. It's front-loaded with the main action and avoids unnecessary words. However, it could be slightly more structured by separating the purpose from the usage context.

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

Completeness2/5

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

Given no annotations and no output schema, the description is incomplete. It doesn't explain what the returned data looks like (e.g., format, structure), any limitations (e.g., max date range), or error conditions. For a tool fetching time-series data, this leaves the agent guessing about the response format and operational constraints.

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 description coverage is 100%, so the schema already documents all four parameters with their types and defaults. The description adds no additional parameter semantics beyond what's in the schema. It mentions 'historical' and 'trends', which loosely relate to date parameters, but provides no extra syntax or format details. Baseline 3 is appropriate when schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Get') and resource ('historical time-series data for a PA indicator'), making the purpose specific. It adds context about identifying trends, which helps differentiate it from simple data retrieval. However, it doesn't explicitly distinguish from sibling tools like 'get_pa_indicator' or 'trend_query', which could be related.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It mentions 'to identify trends', but this is a goal rather than usage context. There's no mention of prerequisites, when not to use it, or how it differs from sibling tools like 'trend_query' or 'get_pa_indicator'.

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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