acceptClarityTeamAssignmentSuggestionAccept a pending Process Landscape team-assignment suggestion and durably assign the suggested team (manager+).
| Name | Required | Description | Default |
|---|---|---|---|
| nodeId | Yes | ||
| suggestionId | 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 not read-only, and the description adds that the assignment is 'durable', clarifying persistence and mutation. It does not disclose whether the accepted suggestion is consumed, whether a previous assignment is overwritten, or any other side effects. Given the annotations, this is adequate but not rich.
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, tightly worded sentence with no filler, tautology, or repetition of the tool name. It front-loads the action and then gives the essential object and outcome, making it easy to scan.
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 simple two-parameter mutation, the description covers the core purpose and effect, and the annotations cover the read-only/destructive expectations. However, it does not explain how to obtain a valid nodeId or suggestionId, nor what happens after acceptance. An agent would likely need external knowledge or sibling tools to invoke this correctly.
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%, and the description does not explain what nodeId or suggestionId refer to beyond their self-descriptive names. It does not clarify where these IDs come from or how they relate to the 'suggested team' mentioned in the description. The description fails to compensate for the complete lack of parameter documentation.
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 ('Accept'), identifies the exact resource ('pending Process Landscape team-assignment suggestion'), and states the resulting effect ('durably assign the suggested team'). It clearly differentiates this from sibling tools like dismissClarityTeamAssignmentSuggestion and assignClarityLandscapeNodeTeam by focusing on accepting an existing suggestion rather than dismissing or directly assigning.
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 the tool is for confirming an already-pending team assignment suggestion, which gives some contextual usage guidance. However, it does not explicitly state when to choose this tool over alternatives, nor does it mention related tools such as dismissClarityTeamAssignmentSuggestion or how pending suggestions are discovered. The usage condition is mostly left to inference.
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.