inviteClarityMeetingNotetakerInvite the in-meeting notetaker bot (Recall.ai) to a live meeting; the recording lands as a 'meeting' capture on the process
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | The clarity process ID | |
| meetingUrl | Yes | ||
| extraCaptureRequestId | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=false, idempotentHint=false, destructiveHint=false – so the agent knows it is a non-idempotent mutation. The description adds that the bot joins 'live' meetings, which implies real-time interaction and that the recording is stored as a 'meeting' capture, providing behavioral context beyond annotations. However, it does not specify if multiple invites are possible, or what happens if the meeting is already being recorded.
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 sentence that clearly conveys the tool's action and outcome. It is appropriately front-loaded with the action. Could arguably include a brief note on prerequisites or parameter format, but for the information provided, it is concise and without wasted words.
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?
With 3 parameters, no output schema, and no enums, the description provides the core action and outcome but omits details like behavior on duplicate invites, timeout constraints, or error scenarios (e.g., invalid meeting URL). The presence of sibling tools like 'inviteClarityOrganizationNotetaker' suggests different scopes, but the description doesn't clarify scope of 'inviteClarityMeetingNotetaker' (individual meeting vs. organization-wide). Adequate but with noticeable gaps.
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 low (33%); two required parameters (id, meetingUrl) are described in the schema but the description adds no additional semantic meaning to them. The optional 'extraCaptureRequestId' is undocumented in the description, so agents must rely solely on the schema. Baseline 3 since the description adds no extra parameter context.
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 uses the specific verb 'Invite' targeting the 'in-meeting notetaker bot' to a 'live meeting', and clarifies the downstream effect ('the recording lands as a 'meeting' capture on the process'). This clearly distinguishes it from sibling tools like 'inviteClarityOrganizationNotetaker' or 'initiateClarityPhoneInterview', which serve different invitation contexts.
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 implies usage when a user wants to record a live meeting via Recall.ai, but does not explicitly state when NOT to use it (e.g., meeting already has a notetaker, or if offline). No guidance on prerequisites (e.g., user must be in the meeting) or alternatives among the many sibling tools is provided.
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.