deleteAutomationSoft-delete an automation. Its member agents and queues stop dispatching and disappear from listings, but their underlying rows and run history are preserved (not erased) so the automation can be restored.
| 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 only declare destructiveHint=true and idempotentHint=true; the description adds substantial nuance by revealing the soft-delete nature: member agents and queues stop dispatching and vanish from listings while underlying rows and run history survive and can be restored. This directly answers what gets destroyed versus what is preserved, going well beyond the structured hints.
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, front-loaded with the verb-resource pair followed by one clause detailing the behavioral consequences. Every sentence earns its place; there is no redundancy or 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 delete operation with rich annotations, the description covers the key questions: what happens to the automation, its member agents and queues, and its underlying data. It omits the response shape and a note on idempotency in prose, but these are either captured by the idempotentHint annotation or minimal for a delete tool.
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% — automation_id is fully documented with type, format, pattern, and a description ('Automation ID'). The description adds no parameter-level detail, so the baseline score of 3 applies; the schema carries the full burden and does so adequately.
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 opens with 'Soft-delete an automation,' which names a specific verb and resource, and immediately distinguishes itself from hard-delete semantics with '(not erased)'. It clearly anchors on automations, separating it from the many other delete* siblings like deleteAgent and deleteQueue, while defining the precise scope of the operation.
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 soft-delete framing plus the explicit consequences — member agents and queues stop dispatching, disappear from listings, and data is preserved for restoration — give an agent a clear picture of when this tool is appropriate. It does not name alternative tools or state exclusions, but the resource is unambiguous and no hard-delete automation alternative exists among siblings, so the context is sufficient.
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.