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Create Pulse Dashboard

createPulseDashboard

Create a new Duvo Pulse dashboard from a natural-language prompt (e.g. 'open cases by queue this week') and dispatch the first generation turn. Generation is asynchronous — poll GET /artifacts/{artifactId} until status is completed. The dashboard is private to you unless you set visibility to 'team', which shares it with your whole team straight away — with permission 'view' (teammates see the dashboard) or 'edit' (teammates can also iterate on it).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
messageYes
permissionNo
visibilityNo
connection_idsNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations establish that this is a non-read-only, non-idempotent create operation, and the description adds valuable behavior beyond that: generation is asynchronous, requires polling GET /artifacts/{artifactId}, and visibility/permission defaults to private unless team sharing is chosen. It does not contradict the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, each earning its place: the core create action, the async polling behavior, and the privacy/permission model. The most important information is front-loaded and there is no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a create operation with no output schema, the description covers the core task, async generation, polling endpoint, and team-sharing semantics. It is slightly incomplete because it does not explicitly state what the initial response contains (e.g. artifactId) or what connection_ids does, but these are inferable or optional.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description must explain the parameters, and it does explain message via the natural-language-prompt example, plus visibility and permission semantics. However, it never mentions connection_ids, leaving the agent to guess its role from the schema alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific action ('Create a new Duvo Pulse dashboard'), specifies the input mode ('from a natural-language prompt'), and states the follow-up behavior ('dispatch the first generation turn'). This clearly distinguishes it from sibling tools like sendPulseDashboardMessage or answerPulseDashboardQuestion, which operate on an existing dashboard.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use it — when a new dashboard is needed from a prompt — but never names an alternative or an exclusion. There is no explicit guidance such as 'use updatePulseDashboard for existing dashboards' or 'use sendPulseDashboardMessage to continue a conversation.'

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

B3.1/5.0
Disambiguation2/5

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.

Naming Consistency4/5

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.

Tool Count1/5

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.

Completeness4/5

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.

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