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Update Case Queue Eval Rubric

updateCaseQueueEvalRubric
Destructive

Edit a single case-level evaluation rubric's title and/or description. The edit produces a NEW rubric (with a new id and slug) so previously judged cases stay attributed to the original criterion; the response contains the new rubric.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleNoShort, human-readable Pass/Fail rubric title.
queue_idYesThe queue's unique identifier
rubric_idYesThe case-level rubric's unique identifier
descriptionNoA 1-2 sentence Pass condition phrased as a question.

TDQS

A4.5/5.0
Behavior5/5

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

The description surfaces a non-obvious behavior beyond the annotations: the edit produces a brand-new rubric with a new id and slug, preserving attribution for previously judged cases. This is critical behavioral context that the annotations alone (readOnlyHint=false, destructiveHint=true) do not capture, and it directly informs the agent of the actual side effect.

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 a single dense sentence that front-loads the verb and resource, then packs the most important behavioral caveat (new id/slug) and response note into the remainder. No filler or redundant restatement of the tool name.

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 tool with only four parameters and full schema coverage, the description provides the essential behavioral nuance—new rubric identity, historical attribution, and response contents. There is no output schema, but the description covers the key return information an agent needs, making the definition complete for selection and invocation.

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 fully documents all four parameters. The description adds little param-level detail beyond restating that title and/or description are the editable fields, which is already evident from the schema. Baseline 3 applies because the schema carries the semantic burden.

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 states a specific action ('Edit'), a specific resource ('a single case-level evaluation rubric'), and the exact fields affected ('title and/or description'). It also distinguishes this tool from the generic updateEvalRubric by clarifying the case-level scope and from createCaseQueueEvalRubric by emphasizing that this edits an existing rubric rather than creating a fresh one.

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 makes the usage context clear: use this when you want to edit an existing case-level rubric's title/description. It doesn't explicitly name alternative tools or state when not to use it, but the edit-versus-create and case-level scoping are enough to guide an agent in most cases.

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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