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zscaler-mcp-server

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by zscaler

zid_list_users

Read-only

List ZIdentity users. Read-only.

Instructions

List ZIdentity users. Read-only.

Returns lean user summaries (id, login name, display name, primary email) rather than the full SDK user record. Pass any of the *_name / email filters for a case-insensitive partial match.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
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.
offsetNo
idp_nameNo
login_nameNo
domain_nameNo
display_nameNo
primary_emailNo
Install Server

TDQS

A4.2/5.0
Behavior4/5

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

Beyond the readOnlyHint annotation, the description adds value by stating the output is a lean summary (id, login name, display name, primary email) rather than the full SDK record, and notes filter behavior (case-insensitive partial match). This is useful behavioral information not in the annotations. It does not contradict the readOnlyHint; it reinforces it. Minor gaps like pagination behavior are not disclosed, but it remains informative.

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 two sentences, front-loads the purpose, and avoids any fluff. Every sentence adds information: the first states the action and read-only nature, the second describes the return shape and filter behavior. This is a model of concise, structured writing.

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?

For a list operation with 8 optional parameters, no output schema, and low schema coverage, the description covers core aspects: purpose, return shape, and filter semantics. It does not explain limit/offset pagination, but these are standard list parameters and the absence is a minor gap. The description is sufficiently informative for most use cases, though it could briefly mention pagination defaults or hint at the query parameter's advanced use (already in schema).

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 only 13%, so the description must compensate. It does explain that the `*_name` and email filters perform case-insensitive partial matches, adding meaning to those parameters. However, it does not address limit/offset semantics or clarify the behavior of the query parameter beyond what its separate schema description already provides. Given the low schema coverage, the description only partially compensates, warranting a 3.

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 opens with 'List ZIdentity users', a specific verb+resource that clearly states the tool's function. It also notes it returns lean user summaries rather than full records, distinguishing it from more detailed retrieval tools. The name itself aligns, and the phrasing sets it apart from siblings like zid_search_users (search) and zid_get_user (single fetch).

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 conveys that filters are optional and perform case-insensitive partial matches, giving clear context on how to narrow results. However, it does not explicitly compare this tool to alternatives like zid_search_users or mention when a search would be more appropriate, so it lacks exclusion guidance. This matches 'clear context, no exclusions'.

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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