declineClarityLandscapeNodeReject a proposed process landscape node while it is still a proposal, removing it and any proposed descendants. Real processes nested underneath survive and move back to Unsorted. Only proposals can be declined — an accepted node is removed with deleteClarityLandscapeNode.
| Name | Required | Description | Default |
|---|---|---|---|
| orgId | Yes | ||
| nodeId | Yes | The proposed node to reject. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description says 'removing it and any proposed descendants' which is a destructive action on proposed nodes, but the annotation destructiveHint=false claims the tool is not destructive. This is a direct contradiction that misleads an AI about the tool's side effects.
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 action and key constraints are front-loaded, and every sentence adds essential information. Perfectly concise for the tool's complexity.
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 low parameter count and absence of output schema, the description covers the main behavior, distinguishes from siblings, and explains edge cases (descendants, accepted nodes). However, it could mention error conditions (e.g., node not found) or return value, and the annotation contradiction detracts from completeness.
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 low at 50% (only nodeId has a description). The description does not explain orgId nor add meaningful detail about nodeId beyond what the schema already provides. With two required UUID parameters, the agent would benefit from context on the org's role, which is missing.
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 action 'Reject a proposed process landscape node' and specifies the resource (proposed node and descendants). It distinguishes from the sibling deleteClarityLandscapeNode by explicitly stating that accepted nodes are removed with that other tool.
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 explicitly states when to use: 'while it is still a proposal' and when not to: 'an accepted node is removed with deleteClarityLandscapeNode'. It also explains the effect on descendants and real processes, providing clear context for decision-making.
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.