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zms_list_policy_rules

Read-only

List ZMS microsegmentation policy rules.

Instructions

List ZMS microsegmentation policy rules.

Read-only. Returns one row per rule (id, name, action, priority, enabled). Filter by name/action. fetch_all bypasses pagination — use sparingly. Requires ZSCALER_CUSTOMER_ID.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNo
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.
actionNo
page_numNo
fetch_allNo
page_sizeNo
Install Server

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, but the description adds meaningful behavior beyond that: one row per rule with specific fields, filter behavior, fetch_all pagination warning, and the requirement for ZSCALER_CUSTOMER_ID. This exceeds the minimum bar set by the 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 compact and front-loaded, covering purpose, read-only status, return shape, filtering, pagination caution, and a required context value in four short lines. Every sentence adds useful information with no fluff.

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?

With no output schema, the description appropriately names the returned fields (id, name, action, priority, enabled) and gives essential operational context (read-only, filters, fetch_all caution, required customer ID). It does not detail page_size/page_num interaction, but the schema defaults cover the pagination basics.

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

Parameters4/5

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

Schema description coverage is low (17%), but the description compensates by explaining that name and action are filters and that fetch_all bypasses pagination. The query parameter is thoroughly documented in the schema itself, while page_num/page_size are self-evident from their names and defaults.

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 the action ('List') and the specific resource ('ZMS microsegmentation policy rules'). It also distinguishes from siblings like zms_list_default_policy_rules by naming the microsegmentation scope and specifying the returned 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?

The description provides clear usage context: it is a read-only listing operation with filters by name/action and a caution about fetch_all bypassing pagination. It does not explicitly name alternatives or exclusion conditions, so it misses the top bar for guidelines, but context is unambiguous.

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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