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Duvo MCP

by io.github.duvoai

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.

Schema Changelog

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

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

The description adds meaningful behavioral context beyond the annotations: the 5-custom-rubric ceiling, the 409 failure mode, and the default live-build behavior. It does not contradict annotations, which already signal a non-read-only write; the more nuanced selected_automation_revision_id behavior is left to the schema.

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 short sentences front-load the core action, then state the most important constraint (5-rubric ceiling with 409) and the default targeting behavior. There is no filler, repetition, or unnecessary detail.

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?

For a 5-parameter write tool with no output schema, the description covers the key failure mode and default target behavior, which is solid. It does not mention the success return value or summarize selected_automation_revision_id's draft/active/historic branching, but that parameter's behavior is fully documented in the schema.

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 even with no parameter enrichment in the description. The description does add default-build semantics for build_id, but this largely restates the schema. It does not meaningfully enrich title, agentId, description, or selected_automation_revision_id beyond what the schema already says.

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 names the specific verb and resource: 'Add one Agent-specific evaluation rubric to a build.' It distinguishes this tool from sibling queue-level rubric tools like createCaseQueueEvalRubric, and from replace/delete/update rubric tools, by specifying the Agent-specific build scope.

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 invocation context: it defaults to the Agent's live build and says to pass build_id to target a specific revision. It does not explicitly name alternatives or state when not to use this tool, but the Agent-specific build framing implies the intended situation.

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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