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Duvo MCP

by io.github.duvoai

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
NameRequiredDescriptionDefault
searchNoFree-text search selecting the cases when all_matching is true.
filtersNoFilters selecting the cases when all_matching is true.
agent_idNoThe agent that should run on the selected cases. Must be a consumer of this queue.
case_idsNoExplicit case IDs to act on (1-100). Provide this or set all_matching.
queue_idYesThe queue's unique identifier
all_matchingNoWhen true, act on every case matching the provided filters/search instead of an explicit id list.

TDQS

A3.9/5.0
Behavior4/5

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

The description discloses key behavioral traits: active runs are interrupted, cases are reset to pending and reassigned. This goes beyond the basic annotations (readOnlyHint=false, destructiveHint=false) by explaining the mutation's nature and side effects. It does not mention rate limits or error conditions, but the core behavior is well-covered.

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 three sentences, front-loaded with the purpose, followed by key behavioral details and a prerequisite. Every sentence is essential and free of fluff. It is highly efficient for an agent to parse.

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

Completeness3/5

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

Given the tool's complexity (6 parameters, nested filters, two selection modes, no output schema), the description covers the core behavior and a prerequisite but omits mention of the two operational modes, batch limits (case_ids max 100), or error handling. It is sufficient for simple use cases but lacks completeness for a fully autonomous agent to use without schema inspection.

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?

Input schema coverage is 100% with clear descriptions for all parameters. The tool description adds no additional parameter-level meaning—it mentions the 'chosen agent' but does not explain the two operational modes (case_ids vs. all_matching) or how filters work. The schema already handles parameter semantics, so the description's contribution is adequate but not enhanced.

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: 'Re-process multiple cases on a chosen agent.' It explains the specific steps (interrupt active runs, reset to pending, assign to agent) and distinguishes it from sibling bulk operations like bulkUpdateCasePriority or bulkUpdateCaseStatus, which update status without reprocessing.

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 provides a prerequisite ('The chosen agent must already be connected to the queue as a case-queue-consumer'), which helps the agent determine if the tool is applicable. However, it does not offer explicit guidance on when to use this tool vs. alternatives (e.g., bulkUpdateCaseStatus, stopRun, startRun) or when not to use it. The context is clear but lacks comparative direction.

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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