getQueueJsonSchemaGet a queue's Case schema settings: whether it requires typed cases, and the schema it validates them against.
| Name | Required | Description | Default |
|---|---|---|---|
| queue_id | Yes | The queue's unique identifier |
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 cover the safety profile (readOnlyHint=true, idempotentHint=true, destructiveHint=false), and the description is consistent with them. The description adds the useful context of exactly what payload the read exposes, but it does not disclose edge behaviors such as what is returned when no schema is attached. No annotation contradiction.
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?
A single front-loaded sentence with zero waste. The verb and resource lead, and the colon-delimited specifics ('whether it requires typed cases, and the schema it validates them against') earn their place by clarifying the return value.
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 one-parameter read with strong annotations and no output schema, the description adequately covers what the tool does and what it returns. Minor gaps remain, such as behavior when no schema is attached, but nothing essential blocks correct invocation.
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%, with queue_id documented as 'The queue's unique identifier' plus uuid format and pattern constraints. The description adds no parameter detail beyond the schema, which is acceptable given the baseline of 3 for high coverage.
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 has a specific verb (Get), a specific resource (a queue's Case schema settings), and precise return semantics (typed-case requirement and validation schema). It is clearly distinguishable from mutation siblings like attachQueueJsonSchema, replaceQueueJsonSchema, and updateQueueJsonSchemaSettings, though it does not explicitly name any sibling for contrast.
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?
Usage context is implied: an agent can infer this tool is for retrieving a queue's case-schema configuration. However, there is no explicit guidance about when to prefer this over closely related tools such as getQueue, listQueues, or the queue-schema mutation tools, and no exclusions are stated.
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.