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Duvo MCP

by io.github.duvoai

Get Revision Case Queue Setup

getRevisionCaseQueueSetup
Read-onlyIdempotent

Check that this build's case-queue integration slots are wired up correctly. Returns, per case-queue-producer/consumer slot, how many queues are linked, plus whether any queue is available to bind (scoped to the agent's automation on a migrated team, team-wide otherwise). A slot with linked_queue_count of 0 is attached but points at no queue and will fail at runtime — link a queue with replaceRevisionIntegrationQueues before starting work.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_idYesAgent ID
build_idYesBuild ID

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already cover the read-only, idempotent, non-destructive safety profile. The description adds substantial behavioral detail beyond that: per-slot queue counts, queue availability scoping based on team migration state, and the runtime failure implication of zero linked queues.

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: purpose first, return semantics second, actionable failure scenario third. The description is front-loaded and contains no filler or redundancy.

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

Completeness5/5

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

Even without an output schema, the description communicates the return contents and even names a field (linked_queue_count). For a read-only diagnostic tool with only two required parameters, an agent has enough context to call it correctly and understand the result.

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?

The schema documents agent_id and build_id with 100% coverage, though the field descriptions are only 'Agent ID' and 'Build ID'. The tool description references 'this build's' and 'agent's automation' but does not add meaningful parameter-level semantics beyond what the schema already provides.

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 function: checking whether a build's case-queue integration slots are wired correctly. It is specific about the resource (case-queue producer/consumer slots), the verb (Check), and the return shape, which distinguishes it from siblings like listRevisionIntegrationQueues.

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

Usage Guidelines5/5

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

The description explicitly says to check before starting work and tells the agent what to do if a slot has linked_queue_count of 0: use replaceRevisionIntegrationQueues to link a queue. This gives both a clear when-to-use condition and a concrete alternative action.

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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