getPulseDashboardHtmlServe a Duvo Pulse dashboard's rendered HTML document (text/html, not JSON). 404 until the first generation turn has produced HTML.
| Name | Required | Description | Default |
|---|---|---|---|
| artifactId | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark readOnly and non-destructive, so the description adds the crucial nuance that the document is only available after the first generation turn and otherwise returns 404. It also discloses the MIME type (text/html), which is not covered by annotations. This is valuable behavioral detail beyond the structured data.
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?
Two short sentences with the key facts front-loaded: what is served, the format, and when it becomes available. There is no filler or repetition, and every clause 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?
For a single-parameter GET-like tool, the description covers purpose, output format, and availability condition. It does not describe the success response body beyond being HTML, but no output schema exists and the agent can infer the content. The missing definition of artifactId is the main gap in completeness.
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 the description does not explain the artifactId parameter or how to obtain it. The tool name and description imply artifactId refers to the dashboard, but this is inference rather than explicit guidance. With only one required parameter, the absence of parameter semantics is a noticeable gap.
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 uses 'Serve' as the verb and specifies the exact resource: a Duvo Pulse dashboard's rendered HTML document. It distinguishes from siblings by explicitly noting 'text/html, not JSON' and the 404-until-generated behavior. This makes the purpose unmistakable.
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 states it serves HTML and not JSON, which implies it should be used when the agent needs the rendered HTML rather than dashboard data. It does not explicitly name alternatives like getPulseDashboard or exportPulseDashboardHtml, but the content-type distinction provides clear context. Lacks explicit when-not-to-use guidance.
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.