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Bulk Create Team Invites

bulkCreateTeamInvites

Invite several people to a team in one call, emailing each invitation immediately. Returns per-batch counts: succeeded were invited, skipped were already on the team, already had a pending invitation, or were a duplicate of an earlier entry in the same batch, failed could not be emailed (those invitations are rolled back). The accept link is always built from the server-configured app origin.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
membersYesPeople to invite, at most 50 per request. An email already on the team, or already holding a pending invitation, is skipped rather than failing the batch.
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.
frontendUrlNoDeprecated. Accepted for backward compatibility and IGNORED: the accept link is always built from the server-configured app origin, so a caller cannot point invitation emails at another host.

TDQS

A4.7/5.0
Behavior5/5

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

The description discloses important behavioral details beyond annotations: immediate emailing, per-batch counts (succeeded/skipped/failed), rollback of failed invitations, and the server-configured accept-link origin. No contradiction with 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?

Three sentences, each carrying essential information: action and emailing, result categorization with rollback, and the accept-link caveat. No filler or redundancy.

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?

Even without an output schema, the description covers the return shape (succeeded/skipped/failed counts), edge cases (skips, duplicate detection), and side effects (immediate email, rollback). Combined with the schema's thorough parameter descriptions, this is fully sufficient for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema coverage, the schema already documents parameter types and constraints. The description adds useful context about batch result semantics (skip reasons, rollback) and reinforces that frontendUrl is ignored, enhancing understanding beyond the raw schema.

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 ('invite'), resource ('several people to a team'), and scope ('in one call'), clearly distinguishing the bulk tool from siblings like createTeamInvite (single invite) and createTeamInviteLink (link-based).

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 clearly implies the intended use case—batch invitations—by emphasizing 'several people in one call' and describing per-batch results. However, it does not explicitly name alternatives or state when not to use this tool, like for a single invitation.

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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