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 |
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?
The description discloses that the schedule fires against the agent's live build, providing some behavioral insight. However, it does not mention side effects such as overwriting existing schedules, whether the action is idempotent (though annotations indicate idempotentHint=false), or what the response contains. Since annotations provide no additional hints (e.g., readOnlyHint, destructiveHint), the description carries the burden but only partially fulfills it.
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 a single, concise sentence that front-loads the primary purpose. It avoids unnecessary words and is well-structured, making it easy to parse and understand quickly.
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?
Given the tool has 9 parameters (4 required) and no output schema, the description is severely incomplete. It does not explain scheduling semantics, how parameters interact, or what the return value looks like. Users would need to consult external documentation or infer from parameter names, which is insufficient for a complete understanding.
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?
The description does not explain any of the parameters beyond the schema's minimal coverage (only agent_id has a description). It does not clarify the meaning of frequency, day, cron, time, recurring, or day_of_month, leaving users to infer their roles. With schema description coverage at only 11%, the description adds no value to parameter understanding.
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 action 'Create a schedule' and specifies the resource 'agent'. It adds context about the schedule firing against the agent's live build and ownership by the authenticated user, which helps differentiate it from other create tools like createAutomation. However, it does not explicitly list the schedule type or frequency options, leaving some ambiguity about the exact 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 does not provide explicit guidance on when to use this tool versus alternatives such as updateSchedule, deleteSchedule, or createAutomation. It does not mention scenarios where scheduling is needed or when other tools might be more appropriate. The only contextual hint is that the schedule fires against the agent's live build, but this is not framed as a usage criterion.
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.