getCaseQueueEvalRubricsList a queue's active case-level evaluation rubrics — the Pass/Fail questions a whole case is judged against at settlement. Shows the queue's current version's set (auto-generated from the connected Assignments' AOPs, plus any manually-authored rubrics).
| Name | Required | Description | Default |
|---|---|---|---|
| queue_id | Yes | The queue's unique identifier |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds valuable behavioral detail beyond annotations by explaining the 'current version's set' and the composition of the rubric set (auto-generated from AOPs plus manually-authored rubrics), which helps an agent understand exactly what data is returned.
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 compact and well-structured: the first sentence immediately states the action and resource, and the second adds essential clarifying context about current version and rubric origin. Every sentence contributes useful information without redundancy or fluff.
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 simple, read-only list operation with a single well-documented parameter, the description provides sufficient context: it defines the domain, the scope ('active', 'current version'), and how the rubrics are generated. No output schema exists, but the description adequately conveys what the result set represents.
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?
There is only one parameter, queue_id, and the input schema description already fully covers it as 'The queue's unique identifier' with 100% schema description coverage. The tool description adds no additional parameter-level meaning, 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 opens with the specific verb 'List' and names the exact resource: a queue's active case-level evaluation rubrics. It clarifies what these rubrics are (Pass/Fail questions a whole case is judged against at settlement), distinguishing this read operation from related mutation siblings like createCaseQueueEvalRubric, updateCaseQueueEvalRubric, and deleteCaseQueueEvalRubric.
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 clear context for when this tool applies: retrieving a queue's current active evaluation rubric set, including auto-generated rubrics from connected Assignments' AOPs and manually-authored ones. It does not explicitly contrast with alternatives such as getEvalRubrics or getEvalScores, but the case-queue scope is clearly conveyed.
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.