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Duvo MCP

by io.github.duvoai

List Automations

listAutomations
Read-onlyIdempotent

List the team's automations, each with its live agent and queue counts. An automation is the workspace container an agent or queue belongs to on a team that has moved to Automations — its id is what POST /queues and POST /agents need as automation_id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoNumber of automations per page (1-250, default 250)
offsetNoNumber of automations to skip
team_idNoDuvo team UUID to operate on. API keys are pinned to a single team — omit this (it falls back to the key's team) or pass that same team; a different team is rejected. OAuth callers, who can span multiple teams, should pass the target team here.

Schema Changelog

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

  1. Added

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds meaningful behavioral context beyond that by stating that results include live agent and queue counts and by explaining the semantic role of an automation id. It does not disclose pagination behavior, but the schema covers the pagination parameters, so the added context is sufficient for a safe read-only list operation.

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?

Two tightly written sentences: the first states the action and result shape, the second explains the domain concept and why the returned id matters. There is no filler, and the most actionable information is front-loaded.

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 simple read-only list tool with no output schema and three fully documented parameters, the description covers the return content (automations with agent and queue counts) and the key domain context (automation_id). It stops slightly short of explicitly stating the full response shape, such as id/name fields, but nothing critical is missing for correct invocation.

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 description coverage is 100%, so the parameters (limit, offset, team_id) are already fully documented in the schema. The description does not repeat parameter-specific details, and the automation_id explanation applies to the tool's output rather than to any parameter. Baseline 3 is appropriate because the schema carries the parameter burden.

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 uses a specific verb ('List') and resource ('the team's automations') and adds concrete detail: each automation comes with its live agent and queue counts. It also defines what an automation is and distinguishes it from agents/queues as a containing workspace, which differentiates it from sibling tools like listAgents and listQueues.

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 gives clear context for when the tool is useful: it identifies the workspace-container role of an automation and tells the caller that the automation id is required by POST /queues and POST /agents as automation_id. It does not explicitly name alternatives like getAutomation or state when not to use this tool, but the use case is clear enough.

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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