updateQueueLabelUpdate a label's key, value, or color. Renaming key/value affects every case assigned to this label.
| Name | Required | Description | Default |
|---|---|---|---|
| key | No | ||
| value | Yes | ||
| label_id | Yes | The label's unique identifier | |
| queue_id | Yes | The queue's unique identifier | |
| color_hue | No |
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 already indicate this is a mutating, non-idempotent, non-destructive operation. The description adds valuable behavioral context by disclosing that renaming key/value affects every case assigned to the label, which is a significant side effect an agent should know before invoking the tool.
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 with no filler. The primary action is stated immediately, and the high-impact side effect is included in the second sentence. Every word contributes useful information.
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 covers the main fields and the key side effect, but for a 5-parameter mutation tool with no output schema, it leaves gaps: no usage guidance, no explanation of key/value semantics, and no note on whether color updates behave differently from key/value updates. The schema and annotations fill some but not all of these gaps.
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 only 40%, with only label_id and queue_id documented. The description mentions key, value, and color, but does not explain what 'key' and 'value' mean in the queue-label context, nor does it clarify the semantics of color_hue beyond the schema's numeric range. This does not sufficiently compensate for the undocumented parameters.
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 clearly states the action ('Update a label's key, value, or color'), identifies the specific attributes that can be changed, and implies the tool targets queue/case labels through the context of assigned cases. It does not explicitly differentiate from sibling updateClarityProcessLabel, but the queue_id parameter and name provide enough grounding.
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 guidance is given about when to use this tool versus alternatives like createQueueLabel, deleteQueueLabel, or updateClarityProcessLabel. The description simply restates the operation without providing selection criteria, prerequisites, or exclusions.
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.