acceptClarityCaptureSuggestionAccept a pending Process Landscape capture suggestion and create the durable 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 already declare the tool is not read-only, not idempotent, and not destructive. The description adds behavioral context by stating it 'creates the durable capture request' and implies the suggestion is consumed (accepted). It also discloses an authorization requirement ('manager+'). This goes beyond what annotations provide, though it could mention side effects like approval workflows.
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 sentence of 15 words, no filler or redundancy. It front-loads the action and resource, making it quick for an agent to parse. Every word contributes to the meaning.
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's simplicity (2 params, no output schema, no enums), the description is adequate for basic invocation but lacks parameter semantics and any mention of return values or side effects. It does not cover what happens after acceptance (e.g., notifications, state changes). The agent may need to infer from context or previous interactions.
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 has 0% description coverage—neither 'nodeId' nor 'suggestionId' are explained. The description hints that 'suggestionId' relates to the capture suggestion and 'nodeId' is likely the landscape node, but it does not explicitly define their roles or constraints. The description should compensate for the missing schema descriptions but falls short.
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 ('Accept'), the resource ('pending Process Landscape capture suggestion'), and the outcome ('create the durable capture request'). It distinguishes itself from sibling tools like dismissClarityCaptureSuggestion and acceptClarityLandscapeNode by specifying the exact type of suggestion and the resulting durable request.
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 implicitly identifies the context—pending landscape capture suggestions—and includes a role constraint ('manager+'), but it does not provide explicit guidance on when to use this tool versus alternatives (e.g., acceptClarityTeamAssignmentSuggestion) or exclude cases (e.g., when to dismiss instead). The usage is clear but not deeply instructional.
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.