attachQueueJsonSchemaPut a queue on an existing Case schema, so both queues share one document and a later change moves them together. The queue must not already have a schema.
| Name | Required | Description | Default |
|---|---|---|---|
| queue_id | Yes | The queue's unique identifier | |
| family_id | Yes |
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 are all false, so they convey only that this is a mutating, non-idempotent, non-destructive operation in a closed world. The description adds meaningful behavioral context beyond that: the queue and Case will 'share one document' and 'a later change moves them together,' plus the precondition that the queue must not already have a schema. It does not mention failure behavior if the precondition is violated.
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 sentences with no filler. The core action is front-loaded, the consequence is explained, and the precondition is delivered in the second sentence. Every word earns its place.
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 conceptual model and precondition are covered, and there is no output schema to document. The main gap is that family_id — one of only two required parameters — is undefined in both the schema and the description, which an agent needs to call the tool correctly. The relationship to createQueueJsonSchema is also left implicit rather than explicit.
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 coverage is only 50%: queue_id is described in the schema but family_id has no description. The tool description does not compensate — it never explains what family_id refers to (presumably the Case schema family), leaving half the required parameters semantically ambiguous for the agent.
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 states a specific action and resource: 'Put a queue on an existing Case schema.' It further explains the resulting shared-document behavior, which distinguishes this from siblings like createQueueJsonSchema (new schema) and detachQueueJsonSchema (remove attachment) without needing to open their schemas.
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 phrase 'existing Case schema' establishes the context: use this when a Case schema already exists and a queue needs to be joined to it, as opposed to creating a fresh schema. It also states the precondition 'The queue must not already have a schema.' However, it does not explicitly name alternatives or say when not to use it.
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.