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Duvo MCP

by io.github.duvoai

Create Case Queue Eval Rubric

createCaseQueueEvalRubric

Add one case-level evaluation rubric to a queue's current rubric set. Case-level rubrics judge the whole case (across every Run that touched it) as a Pass/Fail question at settlement. The rubric is added to the queue's current version (build-set) — the set new cases are judged against; a queue only gets one after its first Agent-processed case settles, so this fails with 409 before then, and once the version holds 12 rubrics.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleYesShort, human-readable Pass/Fail rubric title.
queue_idYesThe queue's unique identifier
descriptionYesA 1-2 sentence Pass condition phrased as a question.

TDQS

A4.4/5.0
Behavior5/5

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

Beyond the annotations, the description discloses important behavioral details: the addition targets the current version/build-set, and the call fails with 409 under two concrete conditions. This gives an agent meaningful operational understanding without contradicting the readOnlyHint/idempotentHint/destructiveHint flags.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The most important action is front-loaded in the first sentence, and each subsequent sentence adds valuable context. Minor redundancy exists between 'current rubric set' and 'current version (build-set),' and the final clause is slightly awkward, so it is not a model of maximum conciseness.

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 three-parameter mutation with no output schema, the description is complete: it explains what the tool does, what a case-level rubric means, where it is added, when it cannot be added, and the numerical cap. No critical information an agent needs to invoke it correctly 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 schema already documents queue_id, title, and description with clear descriptions. The tool description reinforces the Pass/Fail semantics but does not add meaningful parameter-level detail beyond what the schema provides, so the baseline of 3 is appropriate.

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: 'Add one case-level evaluation rubric to a queue's current rubric set.' It clearly distinguishes this tool from siblings like createEvalRubric by scoping it to queue-level, case-level rubrics, and from update/delete/replace siblings by the 'Add one' framing.

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 applies: adding to the queue's current build-set that new cases are judged against. It also states preconditions and failure conditions (409 before the first Agent-processed case settles and once the version holds 12 rubrics), though it does not explicitly name alternatives such as replaceCaseQueueEvalRubrics or createEvalRubric.

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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