List Clarity Process Labels
listClarityProcessLabelsList process tags available in an organization.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| orgId | Yes | ||
| offset | No | ||
| search | No |
listClarityProcessLabelsList process tags available in an organization.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| orgId | Yes | ||
| offset | No | ||
| search | No |
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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the read-only nature is established by structured data. The description adds only the organizational scope ('available in an organization'), which is mildly useful but does not disclose pagination, filtering, or what 'available' means.
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 with no filler, and every word carries purpose. It is concise, though the 'tags' versus 'labels' wording creates minor ambiguity and the terseness contributes to incomplete guidance elsewhere.
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 tool with four parameters, no output schema, and no parameter descriptions, this minimal description is not complete. It fails to clarify the search and pagination parameters, what 'available' means, or how this tool relates to similarly named sibling tools, leaving an agent to guess.
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 compensate for the four parameters, but it only hints at orgId via 'in an organization.' It provides no meaning for limit, offset, or search, leaving the agent without guidance on how pagination and filtering work.
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 ('List'), resource ('process tags'), and scope ('in an organization'), making the basic purpose clear. However, it uses 'tags' instead of 'labels' and does nothing to distinguish this tool from the near-identical sibling listAvailableClarityProcessLabels, so it misses the top score.
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 phrase 'available in an organization' implies this is the tool to use when retrieving org-scoped process labels, so there is some implicit usage guidance. But there are no explicit when-to-use conditions, no exclusions, and no mention of alternatives such as listClarityProcessAssignedLabels or listAvailableClarityProcessLabels.
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.