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Glama

Duvo MCP

by io.github.duvoai

Export Cases

exportCases

Export a queue's cases, respecting the same status, severity, priority, label, date-range, and free-text filters as the list view. format: "json" (default) returns a re-importable JSON payload; format: "csv" returns a text/csv file with the columns id, title, status, priority, labels, created_at, updated_at, completed_at, postponed_to, data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
formatNojson
searchNo
filtersNo
sort_byNocreated_at
queue_idYesThe queue's unique identifier
sort_orderNoasc

Schema Changelog

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

  1. Added

TDQS

A3.8/5.0
Behavior4/5

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

The annotations are weak (all hints are false), so the description carries the burden of behavioral disclosure. It does well by explaining that filters are respected, JSON output is re-importable, and CSV output has a specific column set. It does not mention export limits or async behavior, but it adds meaningful context beyond the schema and annotations.

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 with the core action front-loaded and no filler. Every clause adds operational value: what is exported, which filters apply, and what each format returns.

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 tool with six parameters and no output schema, the description covers the main invocation decisions: queue selection, format choice, filter parity with the list view, and CSV column layout. It leaves some gaps around JSON payload shape, sorting semantics, and export size limits, but it is largely sufficient for correct use.

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 only 17%, and the description compensates for the most important parameter, `format`, as well as giving conceptual meaning to the filter-related parameters by referencing list-view filters. However, `search`, `sort_by`, `sort_order`, and the nested filter fields are left largely to schema names and enums, so the compensation is only partial.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Export') and the resource ('a queue's cases'), and it names the concrete output formats. It does not explicitly contrast with sibling tools like listCases or searchCases, though 'export' makes the distinct purpose fairly evident.

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

Usage Guidelines3/5

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

The phrase 'respecting the same ... filters as the list view' implies when this tool is appropriate and ties it to list-view behavior. The format guidance also helps agents choose JSON vs CSV, but no alternative tool is named and there is no explicit when-not-to-use guidance.

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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