stopClarityArtifactChatConversationStop an in-flight artifact-chat turn. Flips the conversation back to open first so the interrupted run's late webhooks are dropped as stale, then best-effort interrupts the cc-server execution.
| Name | Required | Description | Default |
|---|---|---|---|
| process_id | Yes | ||
| conversation_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are sparse (readOnlyHint=false, idempotentHint=false, destructiveHint=false) but the description adds critical behavioral nuances: it explains the internal order of operations (flipping conversation to 'open' first to drop late webhooks) and labels execution as 'best-effort'. This exceeds what annotations alone convey. However, it does not specify which late webhooks are affected or what happens to partially processed data, so not a 5.
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, each adding distinct value. The first sentence states the core action and its operational consequence. The second explains the internal mechanism. No redundancy, no fluff. It earns its space fully.
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 tool has 2 parameters (both UUIDs), no output schema, and sparse annotations, the description provides enough operational context for an agent to use it correctly. It covers purpose, scope, and internal behavior. However, it could be more complete by mentioning potential failure modes or prerequisites (e.g., 'conversation must be running'). Loses one point for that gap.
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?
The input schema lists two UUID parameters (process_id, conversation_id) but has 0% description coverage. The description does not explain the role of either parameter beyond what the schema already shows (their names and types). Since the schema already conveys the necessary structure and the description is silent on parameter semantics, the baseline of 3 is appropriate.
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 uses a specific verb ('stop') and explicitly names the resource ('in-flight artifact-chat turn'). The context signals confirm 0% schema description coverage, so the description must define the purpose entirely on its own—and it does so clearly and operationally.
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 clearly states the scope ('in-flight') and hints at best-effort semantics ('best-effort interrupts'), which helps the agent decide when to call it. However, it does not explicitly exclude idle or completed conversations, nor does it name sibling tools like stopClarityProcessSnapshot or stopRun for differentiation, so it loses one point.
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.