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Duvo MCP

by io.github.duvoai

List Cases

listCases
Read-onlyIdempotent

List cases in a queue. Supports status, date-range, and free-text filters via query params. Set count_only=true to skip Case row selection and transformation. The normal response shape is returned with cases: [] and the matching total. For label filters, use POST /v2/queues/:queue_id/cases/search.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoNumber of cases per page (1-100, default 20).
offsetNoZero-based offset for pagination.
searchNoFull-text search across case title and data.
statusNoFilter by one or more status buckets (comma-separated). Values: all, pending, processing, needs_input, postponed, needs_review, resolved, canceled. `processing` covers both a case actively being worked and one waiting on its evaluation.
sort_byNoField to sort by. Default: created_at.created_at
priorityNoFilter by one or more priority levels (comma-separated). Values: none, medium, high.
queue_idYesThe queue's unique identifier
count_onlyNoSkip row selection and enrichment. The normal list response shape is returned with an empty row array and the matching total.false
sort_orderNoSort direction. Default: desc.desc
created_at_toNoReturn only cases created before this ISO-8601 timestamp. The upper bound is exclusive.
updated_at_toNoReturn only cases updated before this ISO-8601 timestamp. The upper bound is exclusive.
issue_severityNoFilter by highest failing rubric severity (comma-separated). Values: critical, medium. Severity is a facet within the issues outcome, so it returns nothing when combined with a status bucket that excludes issues. `low` is not selectable: an all-low verdict is stored as success, so no case carries it.
created_at_fromNoReturn only cases created at or after an ISO-8601 timestamp or a lookback such as 24h or 7d.
updated_at_fromNoReturn only cases updated at or after an ISO-8601 timestamp or a lookback such as 24h or 7d.

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so safety is covered. The description adds useful behavioral details: count_only skips row selection/transformation and the response shape includes cases: [] and total. This goes beyond annotations without contradicting them.

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 concise sentences deliver the core purpose, key behavioral nuance (count_only), and the alternative for label filters. No redundancy or filler; information is front-loaded and directly actionable.

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?

Despite 14 parameters and no output schema, the description summarizes the main filter capabilities, explains count_only's effect on response shape, and points to the search endpoint for label filters. It could detail the case fields in the response, but for a list operation the essentials are covered.

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 all 14 parameters are individually documented. The description only provides a high-level grouping of filter types (status, date-range, free-text) and mentions count_only behavior briefly, which adds marginal value beyond the schema. Baseline 3 is appropriate.

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 clearly states 'List cases in a queue' with a specific verb and resource, and distinguishes itself from relevant siblings by noting that label filters should use the search endpoint. It also implicitly differentiates from getCase (single case) and createCases (creation).

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

Usage Guidelines5/5

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

The description explicitly directs users to use POST /v2/queues/:queue_id/cases/search for label filters, providing a clear alternative for a specific use case. It also implies general usage for status, date-range, and free-text filters, which covers the main scenarios.

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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