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zdx_get_deeptrace_cloudpath_metrics

Read-only

Get cloud-path metrics captured during a ZDX deep trace (curated, nested time-series JSON). Read-only.

Instructions

Get cloud-path metrics captured during a ZDX deep trace (curated, nested time-series JSON). Read-only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNoOptional JMESPath expression applied to the results after the API call, for client-side filtering and projection. Examples: "[?enabled==`true`]", "[*].{name: name, id: id}", "length(@)". Omit to get the full records. IMPORTANT: field names are the keys of the returned records, which are usually snake_case (`custom_category`) even where the Zscaler API documents camelCase (`customCategory`) — guessing the spelling yields an empty list that looks like a real answer. If you have not already seen a record from this tool, call it once without `query` and read the keys off the response.
trace_idYes
device_idYes
Install Server

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, and the description repeats the read-only nature. It adds useful context by describing the response shape as 'curated, nested time-series JSON', which is not present in annotations or schema. No contradiction with annotations.

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 a single, front-loaded sentence with no filler content. Every phrase adds value, including the return-format hint and read-only note.

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 lack of an output schema, the description's note about 'curated, nested time-series JSON' provides essential expectation-setting. Combined with the schema's detailed query parameter explanation, this is mostly complete for a straightforward read-only getter, though it omits details like pagination or limits.

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

Parameters2/5

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

With only 33% schema description coverage, the description needed to clarify trace_id and device_id, but it only hints at trace context. The detailed query parameter documentation exists in the schema, not in the description, so the description fails to compensate for the low coverage.

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 uses a specific verb ('Get') and identifies the exact resource ('cloud-path metrics') plus the context ('during a ZDX deep trace'), distinguishing it from sibling tools like zdx_get_deeptrace_cloudpath, zdx_get_deeptrace_health_metrics, and zdx_get_deeptrace_webprobe_metrics.

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 clearly states that this tool returns cloud-path metrics from a deep trace, giving context for when to use it. It does not explicitly name alternatives or exclusions, so it falls short of full usage guidance but is not merely implied.

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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