Assign Clarity Process Labels
assignClarityProcessLabelsAssign process tags to a process.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ||
| labels | No | ||
| labelIds | No |
assignClarityProcessLabelsAssign process tags to a process.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ||
| labels | No | ||
| labelIds | 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 indicate readOnlyHint=false, idempotentHint=false, and destructiveHint=false, so the description must explain side effects. It does not disclose whether assigning labels replaces existing process labels or appends to them, whether labels are created inline or referenced by ID, or what the response looks like. The word 'assign' only signals mutation without adding meaningful behavioral detail.
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 sentence with no filler, making it easy to scan and front-loaded with the main action. It is concise, though it is too sparse to be considered a fully developed definition.
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 three-parameter mutation tool with no output schema and no parameter descriptions, one sentence is not enough. It does not state whether labels and labelIds are alternatives, whether the operation replaces or adds tags, or what happens when only id is supplied, leaving agents unable to invoke it confidently.
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 schema has 0% description coverage and the description adds no parameter-level meaning. It leaves the crucial labels-vs-labelIds distinction ambiguous, does not mention colorHue, and does not clarify that id is required while labels and labelIds are optional in the schema.
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 ('assign') and identifies both the object ('process tags'/'labels') and the target ('a process'), so the core operation is clear. It does not explicitly distinguish itself from close siblings such as createClarityProcessLabel or assignClarityLandscapeNodeTeam, but it is not a tautology or misleading.
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?
No guidance is given about when to use this tool instead of alternatives like assignCaseLabels, assignClarityExtraCaptureRequest, or unlinkClarityProcessLabels. The sentence merely restates the action and provides no conditions, exclusions, or selection criteria.
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.