addClarityLandscapeNodePersonAdd a person involved in the process behind a process landscape node. Name only records a placeholder; supplying an email also gives that person access to the process and emails them an invitation, so only pass an email the user asked you to invite.
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | ||
| role | No | ||
| No | |||
| orgId | Yes | ||
| nodeId | Yes | A process landscape node that is linked to a real process. | |
| teamId | No | ||
| teamRole | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate non-readonly, non-destructive, non-idempotent. The description adds critical behavioral detail: supplying an email gives process access and sends an invitation—a side effect not covered by annotations. Does not mention idempotency or failure modes.
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 sentences: first clearly states purpose, second adds essential behavioral nuance. No redundant or filler content. Every sentence contributes 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?
Covers core action and the email side effect, but lacks information on return value, failure scenarios, or guidance on optional parameter combinations. With no output schema, the agent is left guessing what the tool returns or if it succeeds.
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 14% (only nodeId has a description). The description clarifies the crucial name vs email distinction, but provides no additional meaning for role, teamId, teamRole, leaving their purpose ambiguous. Partial compensation for low schema 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 clearly states the tool adds a person to a process landscape node, using the verb 'Add'. It distinguishes the core action from siblings like batchAdd or remove/update, though it does not explicitly name alternatives.
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 explicit guidance on when to use name vs email, warning that email triggers an invitation and access grant. No mention of when to prefer batchAdd or update over this single-person add, which would improve differentiation.
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.