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Duvo MCP

by io.github.duvoai

Get Batch Queue Stats

getBatchQueueStats
Read-onlyIdempotent

Get case status counts for many Queues in ONE call. Use this after listQueues whenever you need per-Queue counts — cases waiting on a human, needing review, pending, failed — instead of calling listCases or searchCases once per Queue. Results are keyed by Queue ID and include zero counts for Queues with no matching cases; Queue IDs from other teams are silently dropped. For a count on a single Queue with richer filters, listCases with count_only=true is the alternative.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
team_idNoDuvo team UUID to operate on. API keys are pinned to a single team — omit this (it falls back to the key's team) or pass that same team; a different team is rejected. OAuth callers, who can span multiple teams, should pass the target team here.
queue_idsYesComma-separated Queue IDs to aggregate. Include 1 to 100 IDs from the team in the URL.
created_at_toNoExclusive case creation upper bound. Use an ISO 8601 timestamp or a relative duration such as 7d or 12h. Omit it for no upper bound.
created_at_fromNoInclusive case creation lower bound. Use an ISO 8601 timestamp or a relative duration such as 7d or 12h. Omit it for no lower bound.

Schema Changelog

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

  1. Added

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the readOnly/idempotent/destructive annotations, the description discloses important behaviors: results are keyed by Queue ID, Queues with no matches get zero counts, and Queue IDs from other teams are silently dropped. This prevents an agent from assuming errors or missing results in those cases.

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?

Four sentences, each carrying distinct value: what it does, when to use it, what the results look like, and when to use the alternative. The most important scoping fact is front-loaded, and there is no filler or repetition of schema details.

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?

With no output schema present, the description compensates by explaining the response shape: counts keyed by Queue ID, including zero counts. It also covers the auth/team scoping caveat implicitly via the silently-dropped-other-team-IDs note and gives a clear routing alternative, so an agent can select and invoke the tool correctly.

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 coverage is 100%, so the schema already documents queue_ids, team_id, and the date-range parameters. The description adds context about batching and queue scoping, but it does not need to explain parameter formats, and it does not substantially extend the schema's per-parameter meaning.

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 opens with a specific verb, resource, and scope: 'Get case status counts for many Queues in ONE call.' It clearly differentiates itself from siblings like listCases, searchCases, and listQueues by emphasizing batched per-Queue counts.

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?

It explicitly says to use this 'after listQueues whenever you need per-Queue counts' instead of calling listCases or searchCases once per Queue. It also names the alternative for single-Queue counts: 'listCases with count_only=true is the alternative.'

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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