listSkillRevisionsList a skill's version history. Each revision is a snapshot of the skill's files; at most one is active (live) at a time, plus at most one open draft.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of revisions per page (1-100, default 20) | |
| offset | No | Number of revisions to skip | |
| 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, idempotentHint, and destructiveHint false, so the safety profile is covered. The description adds valuable non-obvious context about revision semantics: each revision is a file snapshot, and there is at most one active/live revision and one open draft. This goes meaningfully beyond the structured annotations.
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 two sentences with no wasted words. The primary action is front-loaded, and the second sentence earns its place by explaining the revision/live/draft model that is essential to understanding the result.
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 read-only list tool with strong annotations and complete parameter documentation, the description conveys the essential output concept: a version history of file snapshots with at most one live revision and one open draft. It leaves minor details implicit, such as sort order and whether the draft appears in the paginated list, but these are not significant enough to make the tool hard to call 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 100%, with skill_id, limit, and offset all documented in the schema. The description does not add parameter-level detail, but it does not need to because the schema fully explains the parameters. Baseline 3 is appropriate.
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 clearly states the verb ('List') and the resource ('a skill's version history'), then explains what a revision is: a snapshot of the skill's files with at most one live revision and one open draft. This uniquely distinguishes it from sibling tools like listSkillRevisionFiles, which list files inside a revision, and getRevision, which retrieves a specific revision.
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 explicit when-to-use or when-not-to-use guidance is provided. There is no mention of alternatives such as getRevision or listSkillRevisionFiles, nor any conditions under which this tool should be chosen over them. The domain context implies a listing use case, but an agent must infer the routing decision.
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.