Duvo MCP

Get Org Insights Usage Chart

getOrgInsightsUsageChart

Read-onlyIdempotent

Get org-level run-volume buckets (scheduled vs on-demand) at day/week/month granularity across the organization. Requires an organization Admin, Executive, or Owner role.

Input Schema

TableJSON Schema

Name	Required	Description
`orgId`	Yes	The organization's unique identifier
`endDate`	Yes	End of the chart window (ISO 8601 datetime). Clamped to now.
`timezone`	No	IANA timezone for bucket boundaries (e.g. America/New_York). Defaults to UTC.
`startDate`	Yes	Start of the chart window (ISO 8601 datetime). Clamped to at most 365 days ago.
`granularity`	Yes	Bucket size: day, week, or month

TDQS

A4.2/5.0

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, covering the safety profile. The description adds valuable context beyond annotations: the required organization role (Admin/Executive/Owner) and the output concept (scheduled vs on-demand buckets). No contradiction exists.

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 consists of two tightly packed sentences: one for the core function and one for role requirements. No wasted words, and the most important information is front-loaded.

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 read-only chart retrieval tool with rich annotations and schema, the description covers purpose, scope, output concept, and authorization. There is no output schema, but the return shape (run-volume buckets by granularity) is inferable. The main gap is lack of explicit mention of response structure, but it's not a critical omission given the annotations.

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%, and the schema already explains each parameter thoroughly (ISO format, clamping, timezone default, granularity enum). The description does not add new parameter-level semantics beyond what the schema provides, so the 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 states a specific verb+resource+scope: 'Get org-level run-volume buckets (scheduled vs on-demand) at day/week/month granularity across the organization.' This clearly distinguishes it from sibling tools like getOrgInsightsHeadline and getOrgInsightsMetrics by naming the exact data returned (run-volume buckets) and the granularity options.

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 provides clear context for when to use the tool (when org-level run-volume bucket data is needed) and adds a role requirement, implying it's not for non-admin users. It does not explicitly mention alternatives or state when not to use it, but the scope is unambiguous enough to guide selection.

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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