removeClarityLandscapeNodePersonRemove a person from the process behind a process landscape node, and revoke the process access they were granted when they were added.
| Name | Required | Description | Default |
|---|---|---|---|
| orgId | Yes | ||
| nodeId | Yes | A process landscape node that is linked to a real process. | |
| teamId | No | ||
| personId | Yes | The person entry to change, as returned by listClarityLandscapeNodePeople. |
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 destructive and idempotent, and the description adds meaningful context by specifying that the person's granted process access is also revoked, not just their node association. This clarifies a material side effect beyond what the annotations alone convey. It does not reveal additional limits such as permission requirements or irreversibility, but the annotation coverage lessens that burden.
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, focused sentence that front-loads the primary action and immediately states the key side effect. There is no redundant phrasing or filler, making it easy for an agent to parse 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?
The core action and main side effect are covered, and the annotations plus schema handle required-parameter identification. However, the undocumented teamId parameter, lack of guidance on distinguishing this from removeClarityProcessMember, and no mention of prerequisites or authorization leave the description only partially complete for a destructive operation.
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 input schema describes only nodeId and personId, while orgId and teamId have no description, leaving 50% of parameters semantically uncovered. The tool description does not compensate by clarifying what orgId or teamId represent or how they affect the removal. This is a notable gap for correct invocation.
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 a specific verb and resource combination: 'Remove a person from the process behind a process landscape node,' which clearly identifies the action and target. It also distinguishes itself from related tools by noting the additional consequence of revoking process access, separating it from simple removal or update operations.
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 when to use the tool: whenever a person should be removed from a landscape node and have their related process access revoked. However, it does not explicitly mention alternatives such as removeClarityProcessMember or updateClarityLandscapeNodePerson, nor does it state when not to use this tool.
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.