acceptClarityTeamAssignmentSuggestionAccept a pending Process Landscape team-assignment suggestion and durably assign the suggested team (manager+).
| Name | Required | Description | Default |
|---|---|---|---|
| nodeId | Yes | ||
| suggestionId | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide no behavioral hints (readOnlyHint false, idempotentHint false, destructiveHint false). Description says 'durably assign' but omits side effects, authorization needs, reversibility, or whether it overwrites existing assignments.
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?
Single sentence with 17 words, front-loaded with purpose. However, it could be slightly more structured without adding significant length (e.g., listing parameters).
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?
With no output schema and minimal annotations, the description leaves major gaps: what happens after acceptance, meaning of 'manager+', return value, and prerequisites. A mutation tool requires richer 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% and description does not mention either nodeId or suggestionId, failing to clarify their roles or constraints beyond the schema's empty format definitions.
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?
Description uses specific verb 'accept' and resources 'pending Process Landscape team-assignment suggestion', clearly distinguishing from siblings like dismissClarityTeamAssignmentSuggestion or acceptClarityCaptureSuggestion.
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?
Implies the tool is for pending suggestions but does not explicitly mention when to use accept vs dismiss or other alternatives, nor does it state preconditions like 'only if suggestion exists'.
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.