getPulseDashboardPdfRender a Duvo Pulse dashboard to a PDF document (application/pdf, not JSON). 404 until the first generation turn has produced HTML; 409 for an openui dashboard, whose substrate this exporter cannot render.
| Name | Required | Description | Default |
|---|---|---|---|
| theme | No | light | |
| artifactId | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. The description adds valuable behavioral context: the output is a PDF (not JSON), and errors 404/409 occur under specific conditions. This goes beyond what annotations provide, though it does not cover auth needs or rate limits.
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 sentences, each earning its place: the first states the core purpose and output format; the second adds critical error-case details. No filler, no repetition. The description is appropriately sized and front-loaded.
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 tool with a required UUID parameter and an optional enum, the description lacks parameter explanations. It covers output type and two error conditions but omits prerequisites (e.g., dashboard existence) and does not compensate for the missing schema descriptions. The agent would need to infer or guess the meaning of 'artifactId'.
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%, so the description must explain the parameters. It does not mention 'artifactId' (the dashboard ID) or 'theme' (light/dark) at all. The description adds no meaning beyond the raw schema fields, leaving the agent without guidance on parameter purpose or usage.
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 tool renders a Duvo Pulse dashboard to a PDF document with MIME type application/pdf, distinguishing it from JSON outputs. It also specifies HTTP status codes (404, 409) that clarify state-dependent behavior, adding specificity beyond a generic verb-resource pair.
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 this tool (when a PDF is needed) and includes error conditions (404 before HTML exists, 409 for openui dashboards), but it does not explicitly compare with sibling tools like getPulseDashboardHtml or exportPulseDashboardHtml. Usage guidance is implied rather than explicit.
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.