assignClarityLandscapeCaptureRequestAssign an open Process Landscape capture request to a team member, or unassign it by sending userId: null (manager+).
| Name | Required | Description | Default |
|---|---|---|---|
| nodeId | Yes | ||
| userId | Yes | ||
| requestId | Yes |
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?
Annotations already indicate this is a write operation (readOnlyHint: false) and not destructive. The description adds meaningful behavioral detail by specifying the manager+ permission requirement and the null-userId unassignment mechanism, which are not present in the schema or 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 entire description is one efficient sentence that front-loads the primary action, states the object, and adds the unassignment variant plus permission level. Every clause earns its place with no filler.
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 description covers the core behavior, target state, permission, and null-unassign case, which is enough for tool selection. However, with no output schema and no explanation of nodeId or requestId, an agent may still need domain context to invoke correctly, and there is no mention of behavior if the request is already assigned.
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 0%, so the description must explain the parameters. It clearly explains userId semantics (null means unassign), but nodeId and requestId are left implicit — the description does not state which ID identifies the capture request versus the landscape node. This leaves a meaningful gap for a 3-parameter tool with no schema descriptions.
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 names a specific action (assign/unassign), a specific resource (open Process Landscape capture request), and the target (team member). It also explicitly distinguishes unassignment via userId: null, which differentiates it from sibling tools like assignClarityExtraCaptureRequest or assignClarityLandscapeNodeTeam.
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 the tool applies: the capture request must be open, the caller should be manager+, and unassignment is done by passing userId: null. It does not explicitly name alternative tools or state when not to use it, 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.