batchAddClarityLandscapeNodePeopleAdd one or more people to multiple Process Landscape nodes in a bounded batch. Email entries can create invitations; each node-person outcome reports success, conflict, or delivery failure.
| Name | Required | Description | Default |
|---|---|---|---|
| orgId | Yes | ||
| people | Yes | ||
| nodeIds | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate mutability (readOnlyHint=false) and non-idempotency, but the description adds valuable context: 'Email entries can create invitations' and 'each node-person outcome reports success, conflict, or delivery failure'. This discloses side effects (invitation creation) and potential failure modes beyond what annotations convey.
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 concise sentences with no wasted words. The first sentence states the primary purpose and scope; the second adds behavioral nuance and outcome reporting. Information is front-loaded and every sentence earns its place.
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 no output schema, the description appropriately mentions outcome reporting (success, conflict, delivery failure). The bounded batch hint aligns with schema constraints (maxItems 20). It covers inputs and key behavioral effects. Missing details about return format or idempotency are minor given the tool's batch nature and sibling context.
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 carries the burden. It adds one useful insight: 'Email entries can create invitations', which clarifies the email field's behavioral implication. However, it does not explain orgId, nodeIds, or the people subfields (name, processRole, teamRole). Parameter names are self-evident but the description could add value by detailing constraints or relationships.
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 ('Add one or more people'), the target ('multiple Process Landscape nodes'), and the scope ('bounded batch'). It also mentions outcome reporting, which distinguishes it from the singular sibling tool 'addClarityLandscapeNodePerson'. The verb+resource+scope is specific and 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 implies this tool is for batch operations across multiple nodes, but does not explicitly state when to use the singular alternative or when not to use this tool. The context of 'bounded batch' and listing multiple nodes provides clear usage context, but lacks explicit exclusions or alternatives.
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.