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Duvo MCP

by io.github.duvoai

Delete Case Queue Eval Rubric

deleteCaseQueueEvalRubric
DestructiveIdempotent

Remove a single case-level evaluation rubric. Cases settled after this are no longer judged against it; already-judged cases keep their original verdicts. Removing the queue's last remaining rubric is refused with 409 — whole-case evaluation regenerates rubrics for an empty set at the next settlement, so the removal would not stay removed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queue_idYesThe queue's unique identifier
rubric_idYesThe case-level rubric's unique identifier

Schema Changelog

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

  1. Added

TDQS

A4.3/5.0
Behavior5/5

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

Even though destructiveHint and idempotentHint already flag safety, the description reveals substantive behavior beyond annotations: settled cases retain verdicts, future cases stop being judged, and the 409 refusal is explained with a rationale. This is exactly the kind of context an agent needs for a destructive deletion.

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 carry action, consequence, and edge case with no filler. The most important information is front-loaded, and the 409 exception is explained rather than merely stated.

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 mutation with no output schema, the description covers what happens to future and already-judged cases, the edge case that triggers refusal, and why that refusal exists. Nothing essential for calling or understanding the outcome 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 description coverage is 100%, and the schema already defines queue_id and rubric_id as unique identifiers. The description adds mild context by tying rubric_id to a single case-level rubric and queue_id to the queue's last-remaining-rubric check, but it does not materially extend the schema's parameter semantics.

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 first sentence 'Remove a single case-level evaluation rubric' specifies the action, object, and scope precisely. The queue context and the single-rubric constraint separate it from generic deleteEvalRubric and from create/update/replace siblings.

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 conveys when deletion applies through 'single' and the queue context, and it gives a concrete exclusion: removal of the last remaining rubric is refused. However, it never names alternative tools such as updateCaseQueueEvalRubric or replaceCaseQueueEvalRubrics, so the when-vs-alternatives guidance is only implied.

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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