Duvo MCP

Replace Case Queue Eval Rubrics

replaceCaseQueueEvalRubrics

Destructive

Replace the entire case-level evaluation rubric set on a queue's current version (1 to 12 rubrics). Existing rubrics are removed and the supplied list becomes the new set. An empty list is refused — whole-case evaluation regenerates rubrics for an empty set at the next settlement, so a cleared set would not stay cleared; remove individual rubrics instead. Targets the queue's current version (build-set), which exists once its first Agent-processed case settles — this fails with 409 before then. Cases already judged keep their original verdicts.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`rubrics`	Yes	The complete case-level rubric set for the queue's current version (at least 1 rubric). Replaces every existing rubric.
`queue_id`	Yes	The queue's unique identifier

Schema Changelog

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

AddedAug 28, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Beyond the destructiveHint=true annotation, the description reveals important side effects: existing rubrics are removed, an empty set would be regenerated by whole-case evaluation, the target is the current version, and already-judged cases retain original verdicts. This gives the agent a precise behavioral model with no contradiction to annotations.

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 compact and every sentence earns its place: main action, destructive behavior, empty-list rationale, version precondition and 409 error, and preservation of judged verdicts. The primary function is front-loaded with no filler.

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?

For a destructive replace operation with no output schema, the description covers all critical operational edges: what is replaced, why an empty list is invalid, when the target exists, the error condition, and the effect on existing data. Nothing essential for correct invocation is missing.

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 100%, so the parameters are already fully documented. The description's mention of '1 to 12 rubrics' and 'supplied list becomes the new set' largely restates the schema's minItems/maxItems and replacement semantics rather than adding new parameter-level meaning.

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 action and resource: 'Replace the entire case-level evaluation rubric set on a queue's current version.' The qualifiers 'entire' and 'current version' clearly separate it from sibling create/update/delete single-rubric tools like createCaseQueueEvalRubric or updateCaseQueueEvalRubric.

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?

It explicitly states when not to use the tool ('An empty list is refused') and directs to an alternative ('remove individual rubrics instead'). It also provides a concrete precondition: the operation targets the queue's current build-set version and fails with 409 before it exists. This is clear when/when-not guidance.

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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