replaceQueueJsonSchemaReplace a queue's Case schema with a new version. Every declared field is frozen against automated change, so this is the only way one can be edited.
| Name | Required | Description | Default |
|---|---|---|---|
| schema | Yes | ||
| queue_id | Yes | The queue's unique identifier | |
| change_summary | Yes | ||
| expected_schema_id | 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?
The description adds the key behavioral fact that declared fields are frozen against automated change, making this the sole edit mechanism—context not present in annotations. It omits potential side effects on existing cases/versions and does not mention authorization or rollback, but the annotations already cover the read-only/destructive/idempotent profile, and there is no 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?
Two tight sentences, each carrying information, with the core action first and the rationale second. No filler or redundant restatement of the tool name.
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 multi-parameter mutation with one nested object and no output schema, this is incomplete: expected_schema_id and change_summary are undocumented in both schema and description, and the effect of replacing a schema on existing cases is not addressed. An agent could call it with malformed or insufficient input. However, the frozen-field rule and idempotency annotation do orient the agent.
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 25%; only queue_id has a schema description. The description references 'Case schema' conceptually but does not explain the schema object structure, change_summary requirements, or expected_schema_id's concurrency role, so it fails to compensate for the low 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 states a clear action—replace a queue's Case schema with a new version—and gives distinguishing context: because every declared field is frozen, this is the only editing path. It is not a tautology and separates the tool from generic queue tools, though it does not explicitly contrast with attachQueueJsonSchema/createQueueJsonSchema.
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 implies the trigger condition (when a declared schema field must be edited) and explains why this tool is the only path for that. However, it does not explicitly tell the agent when to prefer attach/create/detach siblings or state when not to use the tool, so the usage guidance is mostly inferred.
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.