deleteAgentDelete an agent and cascade-clean its schedules, case triggers, builder runs, and handover targets. Any active jobs are interrupted and their sandboxes paused.
| Name | Required | Description | Default |
|---|---|---|---|
| agent_id | Yes | The agent's unique identifier |
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 flag destructiveHint=true and readOnlyHint=false, so the description adds purely additive value: it specifies exactly what gets destroyed (schedules, case triggers, builder runs, handover targets) and discloses runtime side effects (active jobs interrupted, sandboxes paused). This is precisely the behavioral context an agent needs beyond the annotation flags.
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 dense sentences with zero waste. The core action and cascade scope are front-loaded in sentence one, and the side effects on active jobs and sandboxes follow in sentence two. Every clause earns its place.
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 destructive one-parameter tool, the combination of annotations (destructive, idempotent) and description (cascade scope, job interruption, sandbox pause) gives an agent nearly everything needed. With no output schema, return behavior is not specified, but the idempotentHint=true annotation partially covers the error/no-op question for already-deleted agents.
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 description coverage is 100% — the single agent_id parameter is already described as 'The agent's unique identifier' with a UUID format. The description adds no parameter-level meaning, so the baseline of 3 applies.
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?
States a specific verb and resource ('Delete an agent') and then details the full scope of what deletion entails via the cascade list. This distinguishes it clearly from partial-deletion siblings like deleteAgentCaseTrigger, deleteAgentFolder, deleteSchedule, and deleteAutomation.
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 context of use is clear: this is the whole-agent destruction path. The cascade-clean detail implicitly signals to an agent that if it only wants to remove a schedule, trigger, or run, a more targeted sibling should be used. However, no alternative is named explicitly and no when-not-to-use conditions are stated.
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.