createScheduleCreate a schedule for an agent. The schedule fires against the agent's live build. The authenticated user owns the schedule.
| Name | Required | Description | Default |
|---|---|---|---|
| day | No | ||
| cron | No | ||
| time | No | ||
| enabled | Yes | ||
| agent_id | Yes | The agent's unique identifier | |
| timezone | Yes | ||
| frequency | Yes | ||
| recurring | No | ||
| day_of_month | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=false, idempotentHint=false, and destructiveHint=false, so the safety profile is clear. The description adds meaningful behavioral context: the schedule fires against the agent's live build and the authenticated user owns the schedule. This goes beyond the annotations by describing ownership and execution target, which is valuable for an AI agent.
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?
The description is two concise sentences, front-loaded with the core action and supplemented by relevant behavioral details. Every word adds value with no redundancy or fluff.
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?
The tool has 9 parameters and no output schema. The description is brief and does not explain the interplay of parameters (e.g., when cron vs time vs day is used), the meaning of frequency, or expected response. Given the complexity, the description is insufficient for an agent to correctly construct a full valid request without additional context.
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 only 11% (only agent_id is described), so the description must compensate by explaining parameter meanings. It does not mention any parameter specifics, leaving many fields (frequency, timezone, cron, etc.) unexplained. The description's mention of 'live build' and ownership does not map to parameters, so it offers little help for invocation.
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 clearly states the specific action: 'Create a schedule for an agent.' It distinguishes from sibling tools like updateSchedule and deleteSchedule by using 'create.' The added context about firing against the live build further clarifies the tool's unique purpose.
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 provides clear context for when to use the tool (to create a schedule) without explicit alternatives. It mentions that the schedule fires against the agent's live build and that the authenticated user owns it, which helps the agent understand implications. However, it does not explicitly mention alternatives like updateSchedule, but the 'create' verb implies the use case.
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.