deleteClarityLandscapeNodeSoft-delete a tree node and its descendants. Organization admins may delete any subtree; team managers may delete a childless process node owned by their team. The targeted node's linked clarity process and captures are deleted, while nested processes detach to the Unsorted bag.
| Name | Required | Description | Default |
|---|---|---|---|
| orgId | Yes | ||
| nodeId | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Goes well beyond the annotations (destructiveHint, idempotentHint) by explaining that it is a soft-delete, that linked clarity processes and captures are deleted, and that nested processes detach to the 'Unsorted bag.' This gives the agent a clear picture of side effects without contradicting 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?
Three tightly packed sentences cover the core action, permission rules, and cascading effects without any fluff. The structure front-loads the primary purpose and then layers constraints and consequences, making it highly efficient.
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?
Given the destructive and complex nature of this operation, the description covers who can delete, what gets deleted, and what happens to nested processes. It lacks explicit handling of return values or post-delete recovery, but since there is no output schema, the essential side-effect information is present.
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?
orgId and nodeId are self-explanatory from their names and UUID types, and the description references 'tree node' and 'subtree' to clarify that nodeId identifies the root of the subtree. However, with 0% schema description coverage, the description does not fully compensate by explicitly mapping parameters or explaining how to obtain them.
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 a specific verb and resource: 'Soft-delete a tree node and its descendants.' It clearly identifies the action (soft-delete) and the target (a node in the Clarity landscape tree), and distinguishes it from sibling delete tools by detailing the cascading behavior and permission nuances.
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?
Provides clear authorization context (org admins vs team managers) and what each role can delete, which helps an agent decide if this tool is appropriate for a given user role. It does not explicitly name alternatives or state when not to use it, but the role-based conditions imply the decision criteria.
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.