list_gmail_labelsList the labels in the user's connected Gmail mailboxes — system labels (INBOX, STARRED, ...) and the user's own labels, with their ids. Use it when the user refers to one of their labels by name.
| Name | Required | Description | Default |
|---|---|---|---|
| account | No | Email address of one connected mailbox. Omit to list labels for all connected mailboxes. |
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?
The readOnlyHint annotation already communicates safety, and the description adds valuable behavioral detail: it returns system labels as well as custom labels and includes their ids. This enriches the agent's expectation of the output beyond what the annotation alone provides.
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 two concise sentences with no redundant wording. It front-loads the tool's core function and then immediately gives the usage context.
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 read-only listing tool with one optional parameter and no output schema, the description is complete enough: it states what is returned (labels with ids), the mailbox scope, and the primary user-intent trigger. The account parameter behavior is documented in the schema.
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%, and the optional 'account' parameter is already fully documented in the schema. The tool description does not add further meaning to the parameter, so the baseline score of 3 is appropriate.
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 specifies a concrete verb ('List'), a precise resource ('labels in the user's connected Gmail mailboxes'), and clarifies the scope by naming system labels and user labels with ids. It is unambiguous and clearly distinct from siblings like list_google_calendars or search_gmail_messages.
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 a clear when-to-use trigger: 'Use it when the user refers to one of their labels by name.' It does not explicitly state when not to use it or name alternative tools, so it stops short of a full 5.
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 are scoped to a distinct resource and action, and descriptions do a good job separating close pairs like search_connections vs ask_about_connections or get_my_linkedin_posts vs linkedin_analytics. However, the multiple deletion tools (delete_graph_object, delete_graph_objects, purge_graph_object) and the several file-reading tools are easy to confuse without reading the descriptions carefully.
The vast majority of tools follow a clear verb_noun pattern such as create_, get_, list_, search_, send_, and delete_. A handful of noun-phrase outliers like linkedin_analytics, mutual_connections, top_message_correspondents, and what_needs_attention break the pattern, so it is highly consistent but not perfect.
64 tools is an extreme count, far beyond the typical well-scoped 3-15 tool range and even beyond the 25+ threshold for 'too many'. While the server covers many integrations, this many tools creates a heavy navigation burden and would be better split into focused servers per domain.
Core graph/CRM operations and read-side integration coverage are strong, with search, get, list, and create tools across most domains. However, there are notable dead ends: no delete_calendar_event, no tracker management beyond create_tracker, and set_follow_up explicitly lacks a read-back query tool, so some natural user requests cannot be completed through the toolset.