Duvo MCP

Stop Pulse Dashboard Message

stopPulseDashboardMessage

Stop an in-flight Duvo Pulse generation turn. Requires edit access to the dashboard. Flips the dashboard to 'interrupted' and keeps any streamed-so-far reply; stopping when nothing is generating is a successful no-op.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`artifactId`	Yes
`partial_text`	No	The assistant text streamed so far, kept in the conversation as the partial reply. Omit when calling from an API client without streaming state.

Schema Changelog

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

First observedAug 17, 2026

TDQS

A4.4/5.0

Behavior5/5

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

Beyond the sparse annotations, the description explicitly discloses side effects: flips the dashboard to 'interrupted', keeps the streamed-so-far reply, and is a successful no-op if nothing is generating. This provides clear expectations about the tool's behavior.

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?

The description is concise and well-structured, consisting of two sentences that efficiently convey purpose, precondition, side effects, and idempotency without unnecessary detail.

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 tool with no output schema, enums, or nested objects, the description covers the essential aspects: purpose, precondition, state change, and no-op semantics. It does not discuss potential error cases or exact response shape, but these are not required given the context.

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?

Schema coverage is 50%: partial_text has a helpful description, but artifactId lacks any description beyond its name and format. The tool description adds no additional parameter clarification. Since one parameter is well-documented and the other is self-explanatory, the score is moderate.

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 clearly states the tool's purpose: to stop an in-flight Duvo Pulse generation turn. It uses a specific verb (stop) and resource (Pulse Dashboard generation), and explains the resulting state change and the no-op behavior, effectively distinguishing it from sibling tools like sendPulseDashboardMessage or answerPulseDashboardQuestion.

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

Usage Guidelines4/5

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

The description implies when to use the tool (to abort an in-flight generation), states a precondition (requires edit access), and explains the behavior when nothing is generating. It does not explicitly contrast with other stop tools (e.g., stopRun) but provides sufficient guidance for this context.

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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