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 |
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 discloses key behavioral traits beyond annotations: it is a soft-delete, it deletes linked clarity process and captures, and nested processes detach to the Unsorted bag. These side effects are critical for the agent to understand the impact. This goes well beyond the destructiveHint annotation and provides specific consequences.
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, front-loaded with the main action and followed by essential details. No filler or redundancy. It efficiently covers the core behavior and conditions.
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 destructive operation with cascading effects, the description covers permissions, the scope of deletion, and the fate of linked and nested entities. It is complete enough for an agent to understand the consequences and prerequisites. The absence of an output schema is acceptable, and the description does not omit critical information.
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 0%, so the description must explain parameter meaning. It explicitly identifies nodeId as the tree node being deleted, but orgId is not directly defined; its role is implied by 'Organization admins.' This partial clarification is insufficient for full compensation, though the context helps infer orgId's purpose.
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 clearly states the tool's function: soft-deleting a tree node and its descendants. It distinguishes this from other delete operations by specifying the scope (subtree vs childless) and the cascading effects on linked processes and captures. The name and description align, and it's unambiguous what resource is acted upon.
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 description provides explicit conditions for usage: organization admins can delete any subtree, while team managers can delete childless process nodes owned by their team. This gives clear permission-based guidance. However, it does not explicitly name alternative tools (e.g., deleteClarityProcess) or state when to choose this over others, though the distinction is implicit.
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.