zendesk_current_userReturn the currently authenticated user — a quick way to verify credentials and subdomain. Zendesk REST: GET /users/me.json.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide readOnlyHint=true; the description adds the REST endpoint (GET /users/me.json) and the practical use of verifying credentials and subdomain. This goes beyond the annotation without contradicting it.
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?
Two concise sentences: the first states the action, the second gives the REST endpoint and use case. Every word earns its place; no redundancy or filler.
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?
For a zero-parameter, read-only 'whoami' tool, the description fully covers purpose, use case, and endpoint. The absence of an output schema is irrelevant since the operation is trivial and self-explanatory.
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?
The tool has zero parameters and the schema is empty, so the description correctly needs no parameter details. The 0-param baseline of 4 applies here.
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 uses a specific verb 'Return' with a clear resource 'the currently authenticated user,' immediately distinguishing it from sibling tools like get_user or search_users. The title 'whoami' further reinforces the self-referential scope.
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?
It explicitly frames the tool as 'a quick way to verify credentials and subdomain,' giving clear guidance for when to use it (auth/session checks). While it doesn't explicitly name alternatives, the current-user scope is obvious among the sibling read tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have distinct purposes, but zendesk_search_users overlaps with zendesk_search (which can search users with type:user), and zendesk_update_ticket can also add a comment, overlapping with zendesk_add_ticket_comment. These create minor ambiguity but are clarified by descriptions.
All tools use a consistent 'zendesk_' prefix and snake_case, but naming patterns deviate slightly: zendesk_current_user lacks a 'get_' verb, and zendesk_execute_view uses 'execute' instead of 'list' or 'get'. Overall, the conventions are mostly uniform and readable.
19 tools is on the higher end but each corresponds to a distinct Zendesk API endpoint or operation. The count is well-scoped for a server covering tickets, users, organizations, views, macros, and satisfaction ratings, without excessive redundancy.
The ticket lifecycle is well covered (create, get, update, comment, list, search, metrics, views), and listing entities like orgs, groups, macros, and fields is supported. Minor gaps exist for user/organization updates and a plain list_users tool, but these are not critical for core support workflows.