dismissClarityCaptureSuggestionDismiss a pending Process Landscape capture suggestion without creating a capture request (manager+).
| Name | Required | Description | Default |
|---|---|---|---|
| nodeId | Yes | ||
| suggestionId | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are minimal (all hints false). The description adds useful behavioral context: that the action is a dismissal (mutation), that it does not create a capture request, and that it requires manager-level permissions. However, it does not disclose side effects (e.g., whether the suggestion is permanently removed, if audit logs are updated, or if notifications occur). For a mutation tool, more thorough behavior documentation would be beneficial.
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, efficient sentence with no filler. It front-loads the verb and resource and appends constraints concisely. Every word 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 that there is no output schema, no parameter descriptions, and no error handling info, the description is too brief. While it captures the high-level purpose, it fails to document parameters, return value, or potential error conditions. The agent lacks sufficient information to correctly supply nodeId and suggestionId or to understand the outcome of the call.
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 0% - the JSON schema properties for nodeId and suggestionId have no descriptions. The tool description does not explain either parameter's meaning (e.g., what nodeId refers to, how to obtain a suggestionId). The agent must infer from parameter names alone, which is insufficient. The description should clarify the role of each parameter to enable correct invocation.
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 ('Dismiss'), the resource ('pending Process Landscape capture suggestion'), and the result ('without creating a capture request'). It also distinguishes from sibling tools like acceptClarityCaptureSuggestion and dismissClarityTeamAssignmentSuggestion by specifying 'capture suggestion'. The role constraint '(manager+)' adds useful context.
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 when to use (dismiss a suggestion you don't want to accept) by contrasting with 'without creating a capture request', but it does not explicitly name alternatives like acceptClarityCaptureSuggestion or provide guidance on when not to use it (e.g., if you intend to accept). Usage is implied rather than explicitly framed.
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.