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Duvo MCP

by io.github.duvoai

Get Eval Scores

getEvalScores
Read-onlyIdempotent

Aggregate evaluation counts and flag distribution for an Assignment's Jobs since the given timestamp.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scopeNoWhen 'custom', only counts Jobs scored against the target revision's custom rubrics, and only `custom__*` rubric flags. Defaults to 'all' (platform + custom rubrics).
sinceYesISO 8601 timestamp; only Jobs evaluated after this are counted
agentIdYesThe agent's unique identifier (Assignment ID)
revision_idNoTarget a specific Assignment revision instead of the live revision. Only honoured when scope='custom' — non-custom scopes aggregate across all revisions. Defaults to the live revision when omitted.

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, which covers the safety profile. The description adds useful context about temporal filtering and aggregation, but it does not disclose the return structure, potential pagination, or behavior when no jobs match the timestamp. This is adequate but not extensive.

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 a single, front-loaded sentence that names the action, object, and scope without redundant wording. Every word earns its place.

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

Completeness3/5

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

Given the tool has no output schema, the description should explain what the returned 'evaluation counts and flag distribution' looks like. It names these concepts but does not clarify their structure, the meaning of 'Jobs,' or edge cases like empty results. Rich parameter documentation partially compensates, but gaps remain.

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 schema provides 100% parameter coverage with clear descriptions for all four parameters, including enum values and formats. The description only restates the 'since' temporal aspect and 'Assignment' (agentId), adding no new semantic meaning beyond what the schema already offers.

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 'aggregate' and identifies the exact resource ('evaluation counts and flag distribution for an Assignment's Jobs') plus a temporal filter ('since the given timestamp'). This clearly distinguishes it from sibling tools like getRunEvaluation or getEvalRubrics, which focus on individual runs or rubric definitions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. It does not mention exclusions, prerequisites, or relationships to other eval-related tools, leaving the agent to infer usage purely from the tool name and resource description.

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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