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Create Eval Rubric

createEvalRubric

Add one Agent-specific evaluation rubric to a build. A build may hold at most 5 custom rubrics; this fails with 409 once that ceiling is reached. Defaults to the Agent's live build; pass build_id to target a specific revision.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleYesShort, human-readable Pass/Fail rubric title.
agentIdYesThe agent's unique identifier (Assignment ID)
build_idNoAdd the rubric to this build (revision). Defaults to the agent's live build when omitted.
descriptionYesA 1-2 sentence Pass condition phrased as a question.
selected_automation_revision_idNoThe automation revision this edit is being made at. Pass a draft and the edit is folded into that draft itself; pass the active revision and the edit lands on a draft branched from it. A historic revision is rejected — it cannot be activated from without an explicit rebase. A revision belonging to a different automation is ignored, and the edit targets the automation the addressed agent belongs to. Omit to target the automation's active revision.

TDQS

A4.2/5.0
Behavior4/5

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

Adds valuable behavioral detail beyond the annotations: the 5-custom-rubric limit, the 409 failure once reached, and the default-to-live-build behavior. The annotations only declare write/non-idempotent/non-destructive, so this extra context meaningfully improves transparency.

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 tight sentences: purpose first, then constraint/failure mode, then default behavior. Every sentence contributes information with no redundancy or fluff.

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

Completeness4/5

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

Covers the core purpose, resource limits, default target, and failure mode. It doesn't mention the selected_automation_revision_id behavior, but the schema description handles that. With no output schema, the absence of return-value details is acceptable.

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% for all 5 parameters, so the schema already documents each field. The description repeats build_id's default-to-live-build behavior but does not add substantially new parameter-level meaning beyond what the schema provides.

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 uses a specific verb ('Add') and resource ('Agent-specific evaluation rubric') targeting a 'build', clearly distinguishing it from update/delete/replace rubric tools. It also scopes the operation to the agent and identifies the default target.

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?

Provides clear context: defaults to the live build, allows targeting a specific revision with build_id, and warns about a 5-rubric ceiling with a 409 failure. It doesn't explicitly name alternatives, but the action semantics and sibling names (update/replace/delete) make the intended use clear.

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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