createQueueJsonSchemaDeclare a queue's first Case schema by hand, so a person can type a queue up front instead of waiting for its producing agent to declare the shape. Fails if the queue already has a schema — replace an existing one with PUT.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | ||
| schema | Yes | ||
| 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?
The annotations already signal a non-read-only, non-idempotent write, and the description adds a concrete failure mode: it fails if the queue already has a schema. It also clarifies that the schema is manually declared rather than agent-generated, adding useful context beyond the annotations.
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 two sentences with no filler. The core purpose is front-loaded, and the failure condition plus replacement guidance follow naturally. Every clause 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?
For a three-parameter create tool with no output schema, the description covers purpose, failure behavior, and replacement routing well. However, the meaning of name and the concrete shape of the schema payload remain underspecified, so it is not fully complete.
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?
Only queue_id is described in the schema, so the description must compensate. It adds meaning by explaining that schema is a hand-declared Case schema and that it must be the first one, but it does not explain the name parameter or the expected structure of the schema object. Partial compensation, not full.
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 identifies a specific action ('Declare a queue's first Case schema by hand'), the exact resource, and the constraint that it is the first schema. It also distinguishes itself from replacement/update siblings by noting the 'first' aspect and the PUT replacement path.
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?
It explicitly says when to use the tool (manual up-front schema declaration before the producing agent declares the shape) and when not to use it (if the queue already has a schema), pointing to replacement via PUT. This gives an agent clear routing guidance among the queue-schema siblings.
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.