Duvo MCP

Bulk Reprocess Cases

bulkReprocessCases

Re-process multiple cases on a chosen agent. Any active runs on the selected cases are interrupted first; the cases are then reset to pending and assigned to the chosen agent for the next dispatcher tick. The chosen agent must already be connected to the queue as a case-queue-consumer (with the trigger enabled or disabled).

Input Schema

TableJSON Schema

Name	Required	Description
`search`	No	Free-text search selecting the cases when all_matching is true.
`filters`	No	Filters selecting the cases when all_matching is true.
`agent_id`	No	The agent that should run on the selected cases. Must be a consumer of this queue.
`case_ids`	No	Explicit case IDs to act on (1-100). Provide this or set all_matching.
`queue_id`	Yes	The queue's unique identifier
`all_matching`	No	When true, act on every case matching the provided filters/search instead of an explicit id list.

Schema Changelog

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

Changed1 schema field changedAug 18, 2026

addedInput schema / properties / filters / properties / issue_severity

Added value: +{
+  "items": {
+    "enum": [
+      "critical",
+      "medium"
+    ],
+    "type": "string"
+  },
+  "type": "array"
+}

First observedAug 17, 2026

TDQS

A4.2/5.0

Behavior4/5

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

The description adds meaningful behavioral context beyond the annotations: active runs are interrupted first, cases are reset to pending, and assignment happens on the next dispatcher tick. This is particularly valuable because the annotations do not convey these side effects, and the destructiveHint=false does not preclude run interruption.

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 convey the action, the side effects, and the prerequisite with no filler. The most important scoping information is front-loaded, and every sentence earns its place.

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?

The description covers the core lifecycle and the key prerequisite for successful invocation. Given the nested filters and selection parameters, the schema carries the parameter details, and the description supplies the operational context that schema cannot. It does not explain return values, but for a bulk action with no output schema, the behavioral flow is the more important 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 description coverage is 100%, so the schema already documents each parameter. The description does not add much parameter-level meaning beyond the overall lifecycle, such as guidance on choosing between case_ids and all_matching. It stays at the baseline appropriate for full schema coverage.

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 verb and resource: 'Re-process multiple cases on a chosen agent.' It further clarifies the behavior by describing the reset and reassignment lifecycle, which distinguishes it from sibling tools like bulkUpdateCaseStatus or startRun.

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 provides a clear operational context and a concrete precondition: the chosen agent must already be connected to the queue as a case-queue-consumer with the trigger enabled or disabled. It does not explicitly contrast with alternatives, but the precondition and lifecycle detail give sufficient guidance for appropriate use.

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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