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Duvo MCP

by io.github.duvoai

Replace Eval Rubrics

replaceEvalRubrics
Destructive

Replace a build's entire Agent-specific evaluation rubric set in one call (at most 5 rubrics). Existing custom rubrics are removed and the supplied list becomes the new set; pass an empty array to clear them. Defaults to the Agent's live build; pass build_id to target a specific revision.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agentIdYesThe agent's unique identifier (Assignment ID)
rubricsYesThe complete custom rubric set for the build. Replaces every existing custom rubric; pass an empty array to clear them.
build_idNoReplace the custom rubrics on this build (revision). Defaults to the agent's live build when omitted.
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.

Schema Changelog

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

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already signal destructiveHint=true, and the description meaningfully elaborates: existing custom rubrics are removed, the supplied list becomes the new set, and empty array clears them. This adds a concrete behavioral consequence beyond the boolean flag, which is exactly what the tool's destructive nature requires.

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 with no wasted words. The primary action is front-loaded, the destructive replacement behavior is stated clearly, and the default/build_id targeting guidance is given compactly. Every sentence earns its place.

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 operation with no output schema, the description covers the essential behavioral facts: whole-set replacement, the 5-rubric cap, clearing via empty array, and build targeting. Combined with the detailed schema and destructive annotation, nothing an agent needs to call this tool 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 description coverage is 100%, so the baseline is 3. The description reinforces the 'entire set' and 'live build default' semantics, but the detailed meaning of each parameter, especially selected_automation_revision_id, is already fully documented in the input schema. The description does not need to compensate for gaps because there are none.

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 verb ('Replace'), a clear resource ('a build's entire Agent-specific evaluation rubric set'), and a precise scope ('in one call (at most 5 rubrics)'). It distinguishes itself from sibling per-rubric tools like createEvalRubric/updateEvalRubric/deleteEvalRubric by emphasizing whole-set replacement rather than individual edits.

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?

It gives clear operational context: the default targets the live build, build_id selects a specific revision, and an empty array clears rubrics. It does not explicitly name alternatives or exclusion conditions, but the 'entire set in one call' framing implies when this tool should be preferred over per-rubric operations.

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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