listSkillAssignmentsList the agents in the current team whose live build references the given skill. Used to warn users that editing a skill will affect every agent that uses it.
| Name | Required | Description | Default |
|---|---|---|---|
| skill_id | Yes | Skill ID. |
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. The description adds valuable context beyond these: the scope is the 'current team' (complementing openWorldHint=false by defining the closed-world boundary), and only 'live build' references count — an important nuance distinguishing current usage from draft or historical revisions.
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 sentences, zero filler. The core function is front-loaded in the first sentence, and the second sentence earns its place by explaining the tool's purpose in an agent's decision workflow. Every word contributes.
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 single-parameter read tool with comprehensive annotations and 100% schema coverage, the description is nearly complete. It specifies scope, the meaning of the result, and when to use it. The only minor gap is the return format, which is not documented since there is no output schema — but 'List the agents…' makes the return shape reasonably inferable.
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 100%, so the baseline of 3 applies — the schema already documents skill_id as a UUID. The description reinforces the semantic ('the given skill' maps to skill_id) but adds no format or syntax details beyond what the schema provides.
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'), a specific resource ('agents in the current team'), and a precise qualifier ('whose live build references the given skill'). It defines what an 'assignment' means in this context, distinguishing it from sibling tools like listAgents, listTeamMembers, listSkillRevisions, and listSkills without needing their schemas.
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 second sentence provides a clear use case: 'Used to warn users that editing a skill will affect every agent that uses it.' This tells an agent when to call the tool (before editing/deleting a skill to assess impact). It doesn't explicitly name alternatives or exclusions, but the context is unambiguous.
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.