assignClarityExtraCaptureRequestAssign (or unassign, with user_id: null) a team member to fulfil an extra-capture request. Restricted to admins and managers.
| Name | Required | Description | Default |
|---|---|---|---|
| user_id | Yes | Team member to assign, or null to unassign | |
| process_id | Yes | The clarity process id | |
| request_id | Yes | The extra-capture-request id | |
| transformation_proposal_id | Yes | The transformation proposal id |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations, the description adds the auth restriction ('Restricted to admins and managers') and clarifies the unassignment behavior. No contradiction with annotations. It does not detail side effects or return behavior, but is transparent about the main operation.
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, front-loaded sentence that efficiently covers the action, unassignment option, and access restriction. No wasted words, clear and direct.
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's complexity (4 required params, no output schema), the description covers the core operation, unassignment, and auth. It does not describe return values or side effects, but these are not critical for a straightforward assignment 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 description coverage is 100%, so parameters are already documented. The description repeats the null assignment behavior for user_id, which is already in the schema, adding no new semantic value. Baseline score of 3 is appropriate.
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 (assign/unassign), the target (team member), and the resource (extra-capture request). It distinguishes from siblings like assignClarityLandscapeCaptureRequest by specifying 'extra-capture request'.
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 for extra-capture requests and mentions unassignment with null, but does not explicitly differentiate from sibling tools or provide when-not-to-use guidance. The context is implicit rather than explicit.
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.