listClarityArtifactChatConversationsList the current user's recent non-deleted artifact-chat conversations for a Clarity v2 process and snapshot kind.
| Name | Required | Description | Default |
|---|---|---|---|
| process_id | Yes | The Clarity process id | |
| snapshot_kind | Yes | Artifact kind to list conversations for |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, idempotentHint, and destructiveHint=false. The description adds behavioral traits beyond annotations: it specifies 'recent' (implying temporal ordering), 'non-deleted' (filtration), and 'current user's' (scope). It does not disclose pagination, max count, or ordering, but the added context is valuable and consistent with the annotations.
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 a single, well-structured sentence that front-loads the verb and resource. Every word is meaningful and non-redundant. No filler or irrelevant details.
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 fully covered by schema, good annotations, and no output schema, the description sufficiently covers what the tool does and its inputs. It adds scope and filtration context. However, it omits details about the return structure (e.g., fields in the conversation list), which would be helpful for an agent since there is no output schema. Still, the description is complete enough for most selection and invocation decisions.
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 100%: both process_id and snapshot_kind have descriptions in the schema. The description does not add new parameter semantics beyond restating 'for a Clarity v2 process and snapshot kind'. Since the schema fully documents the parameters, the description adds no extra meaning, warranting the baseline score of 3.
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 verb 'List', the resource 'artifact-chat conversations', and specifies scope: 'current user's recent non-deleted' and conditioning on 'Clarity v2 process and snapshot kind'. This uniquely identifies the tool's function and distinguishes it from sibling tools like deleteClarityArtifactChatConversation and getClarityArtifactChatMessages.
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 usage context (when you need conversations for a given process and snapshot kind) but does not provide explicit guidance on when to use this tool versus alternatives (e.g., getClarityArtifactChatMessages for messages within a conversation, or listClarityProcessSnapshots for snapshots). No exclusion criteria or when-not-to-use advice is given.
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.