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Duvo MCP

by io.github.duvoai

Get Eval Rubrics

getEvalRubrics
Read-onlyIdempotent

List the evaluation rubrics a Run is scored against: the platform default rubrics plus the Agent-specific rubrics for a build. Defaults to the Agent's live build; pass build_id to target the revision a specific Run ran against.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agentIdYesThe agent's unique identifier (Assignment ID)
build_idNoReturn rubrics for this specific build (the revision a Run ran against). Defaults to the agent's live build when omitted.

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is known. The description adds meaningful behavior beyond annotations by specifying that the result includes platform default plus Agent-specific rubrics and by explaining the build defaulting logic.

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 compact sentences: the first states exactly what the tool lists, and the second explains the default and override 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.

Completeness5/5

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

For a simple 2-parameter, read-only listing tool with complete schema descriptions and clear annotations, the description fully covers purpose, output contents, and default/override behavior. The 'List' verb adequately implies the return shape even without an output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already has 100% coverage with detailed descriptions for both parameters, including UUID format and build_id's default behavior. The description reinforces the meaning of build_id ('Defaults to the Agent's live build; pass build_id to target the revision a specific Run ran against'), adding value beyond the schema.

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 the specific verb 'List' and identifies the exact resource: 'the evaluation rubrics a Run is scored against.' It also clarifies composition ('platform default rubrics plus the Agent-specific rubrics for a build') and the default/override behavior, distinguishing it from related tools like getEvalScores or getRunEvaluation.

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 gives clear usage context: it defaults to the Agent's live build and explains how to pass build_id to target a specific Run's revision. It does not explicitly name alternative sibling tools, but the guidance is sufficient for a read-only retrieval operation.

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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