list_usersRetrieve users in the current organization, optionally filtering by name or email to locate specific people.
List users in the current organization.
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | Filter by name (case-insensitive substring match) | |
| No | Filter by email address (case-insensitive substring match) |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.8.0Input schema / properties / emailAdded value: +{
+ "anyOf": [
+ {
+ "type": "string"
+ },
+ {
+ "type": "null"
+ }
+ ],
+ "default": null,
+ "description": "Filter by email address (case-insensitive substring match)"
+}Input schema / properties / nameAdded value: +{
+ "anyOf": [
+ {
+ "type": "string"
+ },
+ {
+ "type": "null"
+ }
+ ],
+ "default": null,
+ "description": "Filter by name (case-insensitive substring match)"
+}v0.3.2Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral disclosure burden. It implies a read operation but does not state permissions, pagination behavior, rate limits, or whether inactive users are included.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single front-loaded sentence with no wasted words. It is appropriately concise for a simple list tool, though it is arguably too sparse given the absence of annotations.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple, has an output schema, and its parameters are fully documented in the schema, so the description need not explain return values. However, with no annotations and no usage guidance, the description leaves meaningful behavioral and selection context unaddressed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so both the name and email filters are fully documented in the schema. The description adds no parameter meaning beyond what the schema already provides, which matches the baseline 3 when schema coverage is high.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear verb ('List'), resource ('users'), and scope ('current organization'). It distinguishes the tool from get_user by plural listing, though it does not explicitly name or contrast with any sibling tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no guidance on when to use this tool versus alternatives such as get_user or list_organizations. It only states what the tool does, leaving usage context to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.