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 automation proposal id |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / transformation_proposal_id / descriptionPrevious value: -"The transformation proposal id"New value: +"The automation proposal id"Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=false and destructiveHint=false, and the description adds useful behavior beyond that: the null-to-unassign semantic and the admin/manager restriction. It does not describe side effects on request status, but the core mutating behavior and permission boundary are disclosed without contradicting annotations.
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 sentences with no filler. It front-loads the primary action, then immediately covers the null unassignment case and the permission restriction. Every sentence 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 four-parameter mutation tool with no output schema, the description covers the essential context: what action to perform, the special null case, and who is allowed to perform it. It could add more context about workflow sequence or relationship to sibling tools, but it is largely complete for invoking the tool correctly.
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%, and each parameter already has a clear description. The description mostly reiterates the `user_id: null` unassignment semantics already present in the schema, and adds no extra meaning for process_id, request_id, or transformation_proposal_id. Baseline 3 is appropriate because the schema handles parameter documentation.
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 states a specific verb and resource: 'Assign (or unassign, with `user_id: null`) a team member to fulfil an extra-capture request.' It clearly identifies what the tool does and differentiates it from sibling tools like assignClarityLandscapeCaptureRequest by the explicit 'extra-capture request' target.
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 gives clear context for when to use the tool: to assign or unassign a team member, with the unassign condition explicitly tied to `user_id: null`. It also states the role restriction ('Restricted to admins and managers'). It does not explicitly name alternative tools or when not to use them, so it stops short of a 5.
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.