updateClarityLandscapeNodePersonChange the role a person plays in the process behind a process landscape node (for example "Approver"). Send role: null to clear it.
| Name | Required | Description | Default |
|---|---|---|---|
| role | No | ||
| 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 carry write intent (readOnlyHint=false) and non-destructiveness (destructiveHint=false). The description adds one non-obvious behavioral detail: null clears the role rather than being an invalid value. It does not disclose side effects, permissions, or the outcome for the person assignment when the role is cleared.
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?
Two tight sentences with no filler. The core action is front-loaded, and the second sentence efficiently communicates the important null-clearing behavior.
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?
Adequate for a simple write operation with no output schema: the description covers the intent and the key role nuance, and the schema documents nodeId and personId. However, it omits expected return behavior, failure conditions, and any guidance for the optional teamId parameter.
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 only 40%, so the description needs to compensate. It does add real meaning for the role parameter by explaining it semantically and giving an example, but it does not describe orgId or teamId, leaving those to be inferred from their names.
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?
States a specific verb ('Change'), a precise object (the role a person plays in the process behind a process landscape node), and gives a concrete example ('Approver'). This clearly distinguishes it from sibling add/remove/list tools even without naming them.
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?
Provides useful operational guidance ('Send role: null to clear it') but does not explicitly say when to prefer this tool over addClarityLandscapeNodePerson or removeClarityLandscapeNodePerson. The intended usage is inferable from the action 'change the role', but no alternative routing is stated.
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.