updateQueueUpdate a queue.
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | New queue name. Must be unique within the team. This route is refused once a team is on automation revisions, where a queue is instead renamed through its revision binding and uniqueness is checked per revision on activation. | |
| queue_id | Yes | The queue's unique identifier | |
| folder_id | No | New folder id, or null to move to the root. | |
| description | No | New description, or null to clear. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / name / descriptionPrevious value: -"New queue name. Must be unique within the team. This route is refused once a team has moved to Automations, where a queue is instead renamed through its revision binding and uniqueness is checked per revision on activation."New value: +"New queue name. Must be unique within the team. This route is refused once a team is on automation revisions, where a queue is instead renamed through its revision binding and uniqueness is checked per revision on activation."Input schema / properties / name / descriptionPrevious value: -"New queue name. Must be unique within the team."New value: +"New queue name. Must be unique within the team. This route is refused once a team has moved to Automations, where a queue is instead renamed through its revision binding and uniqueness is checked per revision on activation."Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate this is a mutating, non-idempotent operation, but the description adds no behavioral context beyond that. It does not disclose what changes are applied, whether the operation replaces fields wholesale, any side effects, or permission requirements.
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 extremely short but not appropriately sized for a four-parameter mutation tool. It contains only a single tautological sentence and omits informative content that would help an agent invoke it correctly.
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 description is incomplete for the tool's complexity. With no output schema and sparse annotations, the burden falls on the description, but it fails to explain update semantics, alternatives, or operational constraints, leaving the agent to rely entirely on 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%, so the input schema already documents each parameter thoroughly, including the special automation-revisions constraint on name. The description itself adds no parameter-level semantics, keeping this at the baseline of 3.
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 'Update a queue.' is a tautology—it restates the tool name and title without specifying what aspects of the queue can be updated, what the effect is, or how this differs from createQueue/deleteQueue/getQueue. It provides no detail beyond the verb and resource.
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?
No usage guidance is provided. The description does not mention when to use this tool instead of createQueue, deleteQueue, or other queue-related tools, nor does it indicate prerequisites or constraints such as the automation revisions restriction described in the schema.
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.
Despite detailed descriptions, many tool names are highly ambiguous, with multiple tools covering the same conceptual actions (e.g., acceptClarityCaptureSuggestion vs. acceptClarityTeamAssignmentSuggestion, or the many deleteClarity*Interview tools). The set is so large that distinguishing between, say, listClarityFolders, listClarityProcesses, and listClarityProcessSummaries requires reading deep into descriptions, reducing agent selection accuracy.
The naming convention is predominantly verb_noun (e.g., createClarityProcess, listAgents, deleteQueue), and is remarkably consistent across the 316 tools. There are only minor deviations, such as 'fileSuggestedClarityProcesses' (verb + adjective noun) and 'bulkUpdateCasePriority' (where 'bulk' could be seen as a prefix), but overall the pattern holds strongly.
With 316 tools, this server is extremely oversized for any single agent to manage effectively. The massive number of tools suggests poor modularization—many of these tools likely belong in separate, smaller servers focused on specific domains (e.g., Clarity, Pulse, Agent management). The cognitive load for an agent to choose from 316 options is very high, leading to frequent misselection.
The tool surface covers an extraordinarily wide range of operations across the Duvo platform: agents, runs, cases, queues, Clarity processes, skills, integrations, notifications, teams, and more. Most resource types have full CRUD and lifecycle management. Notable minor gaps exist (e.g., no tools for managing specific notification batch severities dynamically, and some interview management is missing batch operations), but for the platform's scope, coverage is impressively thorough.