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Glama

Duvo MCP

by io.github.duvoai

Create Queue

createQueue

Create a new queue.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesQueue name. A standalone queue must be uniquely named within the team. A queue created inside an automation instead has to be uniquely named within a revision, which is checked when that revision is activated.
team_idNoDuvo team UUID to operate on. API keys are pinned to a single team — omit this (it falls back to the key's team) or pass that same team; a different team is rejected. OAuth callers, who can span multiple teams, should pass the target team here.
folder_idNoOptional folder to nest the queue under.
descriptionNoOptional human-readable description.
automation_idNoAutomation this queue belongs to. Optional for teams not yet migrated to Automations; required once a team has migrated.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedInput schema / properties / name / description
      Previous value: -"Queue name. Must be unique within the team."New value: +"Queue name. A standalone queue must be uniquely named within the team. A queue created inside an automation instead has to be uniquely named within a revision, which is checked when that revision is activated."
  2. First observed

TDQS

C2.9/5.0
Behavior2/5

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

The annotations already indicate readOnlyHint=false, destructiveHint=false, and idempotentHint=false, so the agent knows this is a non-read-only operation. The description adds no additional behavioral detail about side effects, idempotency implications, permissions, or failure modes. It contributes nothing beyond the annotations already provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence with no filler, and the core action 'Create a new queue' is front-loaded. It is appropriately concise, though it sacrifices useful context that would justify a perfect score.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With five parameters, no output schema, and many queue-related sibling tools, a single generic sentence is insufficient for an agent to fully understand the operation's context, return value, or prerequisites. The schema fills in some gaps, but the description alone is inadequate for complete tool selection and invocation.

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?

The input schema has 100% description coverage for all five parameters, including detailed notes on name uniqueness, team_id fallback behavior, and automation_id migration requirements. The description itself adds no parameter-level information, but the schema fully carries that burden, 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.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Create a new queue' clearly identifies the action (create) and the target resource (queue), making it distinct from similar siblings like createQueueAggregation, createQueueLabel, and createQueueJsonSchema. It is direct and unambiguous, though it does not explain what a queue is or when it should be used.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives such as updateQueue, deleteQueue, or listQueues. It also omits important contextual conditions like the automation migration requirement mentioned in the automation_id parameter description. An agent receives no direction for choosing this tool correctly.

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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TDQS

B3.1/5.0
Disambiguation2/5

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.

Naming Consistency4/5

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.

Tool Count1/5

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.

Completeness4/5

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.

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