listClarityLandscapeNodePeopleList the people involved in the process behind one process landscape node. To read the people across the whole landscape in one request, use getClarityHierarchyPeople instead.
| Name | Required | Description | Default |
|---|---|---|---|
| orgId | Yes | ||
| nodeId | Yes | A process landscape node that is linked to a real process. | |
| teamId | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, idempotentHint, and destructiveHint, covering the core behavioral traits. The description adds scope context (single node vs. whole landscape) but does not disclose additional behavioral details like pagination, rate limits, or data freshness. With annotations present, the description adds minimal extra value.
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 wasted words. The purpose is front-loaded, and the alternative tool reference is efficiently placed in the second sentence. Every sentence adds value.
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 simple list tool with three parameters, good annotations, and no output schema, the description covers the essential functionality and usage boundaries. It does not describe the return format or potential empty results, but given the tool's clarity, it is nearly complete. A minor gap is the lack of indication that results are a list of people.
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 description coverage is only 33% (only nodeId has a description). The tool description does not elaborate on the meaning of orgId, nodeId, or teamId beyond what the schema provides. Given the low coverage, the description should compensate but does not, leaving potential confusion about parameter roles.
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 'List the people involved in the process behind one process landscape node' with a specific verb and resource. It also explicitly distinguishes from the sibling tool 'getClarityHierarchyPeople' by noting the alternative for whole-landscape queries, making the purpose unambiguous.
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 clear guidance on when to use this tool (for a single node) and when to use the alternative (getClarityHierarchyPeople for whole landscape). It does not list prerequisites or exclusions beyond the nodeId schema note, but the context is sufficient for typical use.
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.