getAutomationTabsGet the automation detail view's tab arrangement, plus metadata for the Pulse dashboards pinned into it. Pinned dashboards are filtered to the ones the calling identity may see.
| Name | Required | Description | Default |
|---|---|---|---|
| automation_id | Yes | Automation ID |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only, idempotent, non-destructive behavior. The description adds one useful behavioral trait beyond those: pinned dashboards are filtered by the calling identity's visibility, which is important context an agent could not infer from the schema or annotations. It does not detail return-value shape, but the most safety-relevant behavior is covered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences carry the full purpose: main resource, additional returned data, and an important filtering caveat. It is front-loaded with the primary action and contains no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter, read-only tool, the description covers the input (through schema), the output at a useful level (tab arrangement plus each pinned dashboard's metadata), and a visibility filter that affects the result set. No output schema exists, but the high-level return contract is sufficient for an agent to 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.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single required automation_id parameter, which already documents its type, format, and meaning. The description adds no param-specific syntax or context, so it warrants the baseline 3 rather than more.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a precise verb-resource pair: it retrieves the automation detail view's tab arrangement plus metadata for pinned Pulse dashboards. It also adds a distinguishing scoping detail (dashboards are filtered by what the caller may see), which separates it from generic getAutomation or listPulseDashboards calls.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly communicates what this tool returns, so an agent can infer it is the right choice when it needs automation detail tab layout or pinned Pulse dashboard metadata. It does not explicitly identify sibling alternatives or state when not to use it, but the content is specific enough to avoid obvious confusion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.