refreshPulseDashboardTrigger an on-demand data refresh of a Duvo Pulse dashboard. The dashboard regenerates its connection-sourced values in the background — poll GET /artifacts/{artifactId} for status.
| Name | Required | Description | Default |
|---|---|---|---|
| artifactId | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds meaningful behavioral detail beyond the annotations: the refresh is asynchronous, runs in the background, and requires polling GET /artifacts/{artifactId} for status. This aligns with readOnlyHint=false, idempotentHint=false, and destructiveHint=false, with no contradiction.
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 concise, front-loaded sentences convey the trigger action, the background behavior, and the follow-up polling step. Every sentence earns its place with no redundant or filler content.
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 async mutation with no output schema, the description gives the essential workflow: trigger refresh, wait in background, poll for status. It could mention what the initial trigger response looks like or how long polling might take, but it is adequately complete for the tool's complexity.
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 schema provides only artifactId with UUID format and 0% schema description coverage. The description compensates by referencing artifactId in the polling URL, indicating the parameter identifies the artifact whose refresh status should be checked. Since there is only one parameter, this is sufficient.
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 states a specific action ('Trigger an on-demand data refresh'), a specific resource ('Duvo Pulse dashboard'), and the resulting behavior ('regenerates its connection-sourced values in the background'). This clearly distinguishes it from related siblings like updatePulseDashboard, createPulseDashboard, or refreshQueueAggregation.
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 makes the use case clear: refresh the dashboard's connection-sourced data on demand, then monitor via polling. It does not explicitly discuss alternatives or when not to use it, but the context is specific enough that an agent can determine when this tool is appropriate.
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.