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Duvo MCP

by io.github.duvoai

Update Eval Rubric

updateEvalRubric
Destructive

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleNoShort, human-readable Pass/Fail rubric title.
agentIdYesThe agent's unique identifier (Assignment ID)
rubricIdYesThe custom rubric's unique identifier
descriptionNoA 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?

Annotations declare readOnlyHint=false and destructiveHint=true, but the description adds crucial non-obvious behavior: 'The edit produces a NEW rubric (with a new id and slug).' This clarifies that the original rubric is preserved for run attribution and that the response contains the new rubric. No contradiction with 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 two sentences: the first states the purpose, the second reveals the key side effect and response. Every word earns its place, with no filler or redundancy. It is front-loaded and easy to scan.

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?

Given 5 parameters and no output schema, the description effectively covers the critical side effect and response behavior. The complex selected_automation_revision_id parameter is fully documented in the schema, so the description need not repeat it. Overall, the combination of description, annotations, and schema provides sufficient context.

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?

The input schema covers all 5 parameters with detailed descriptions (100% coverage), so the baseline is 3. The description adds minimal parameter-level meaning, only implying that title and description are the editable fields. No extra guidance beyond the schema is offered.

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: 'Edit a single Agent-specific evaluation rubric's title and/or description.' It clearly differentiates from sibling tools like createEvalRubric, deleteEvalRubric, and replaceEvalRubrics by emphasizing it edits only title/description and produces a new rubric. The core action and scope are unambiguous.

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 provides clear context: 'previously scored Runs stay attributed to the original criterion' explains why you would use this tool instead of replacing or deleting rubrics. However, it does not explicitly name alternatives or state when not to use this tool, stopping short of a full 5.

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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