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Attach Revision Integrations

attachRevisionIntegrations

Attach one or more integrations to an agent revision. To pin specific connections after attachment, use pinRevisionIntegrationConnection. IMPORTANT for the case-queue-producer and case-queue-consumer integrations: attaching the integration alone is NOT enough — the slot points at no queue and will fail at runtime until you link at least one queue with replaceRevisionIntegrationQueues. After wiring up, call getRevisionCaseQueueSetup to confirm every case-queue slot has linked_queue_count > 0 before starting work.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_idYesAgent ID
build_idYesBuild ID
integration_idsYesIntegration IDs (or custom integration IDs) to attach to the revision
selected_automation_revision_idNoThe automation revision this edit is being made at. Pass a draft and the edit is folded into that draft itself; pass the active revision and the edit lands on a draft branched from it. A historic revision is rejected — it cannot be activated from without an explicit rebase. A revision belonging to a different automation is ignored, and the edit targets the automation the addressed agent belongs to. Omit to target the automation's active revision.

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior4/5

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

The description discloses a critical non-obvious behavior: attaching case-queue integrations alone leaves the slot pointing at no queue and will fail at runtime until queues are linked. It also recommends a verification step. While annotations already indicate this is a mutating operation (readOnlyHint=false), the description adds meaningful behavioral caveats beyond the structured 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, front-loaded with the core action, then the alternative tool, then the important workflow caveat. Every sentence earns its place; no fluff 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?

Given the tool's complexity and lack of an output schema, the description is remarkably complete: it states the action, routes to the correct sibling for pinning, warns about a runtime failure mode, and tells the agent how to verify success. An agent has enough context to invoke this correctly and know when additional steps are required.

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?

Schema coverage is 100%, so the baseline is 3, but the description adds meaningful semantics for integration_ids by singling out case-queue-producer and case-queue-consumer integrations and explaining their runtime requirements. This goes beyond the schema's generic 'Integration IDs...' description.

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 the verb and resource: 'Attach one or more integrations to an agent revision.' It also distinguishes itself from the sibling pinRevisionIntegrationConnection by explaining that pinning is a separate follow-up action. This is specific enough for an agent to know exactly what the tool does.

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 pinRevisionIntegrationConnection for pinning connections after attachment. It also gives a clear workflow for case-queue integrations: attach → link queues via replaceRevisionIntegrationQueues → verify with getRevisionCaseQueueSetup. This is explicit when-to-use and when-to-use-alternative 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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