listSkillRevisionFilesList the files in a skill revision, one page at a time. When has_more is true, pass next_cursor back as cursor to fetch the next page.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of files to return per page (1-1000, default 100) | |
| cursor | No | Opaque continuation token from a previous response's `next_cursor`. Omit for the first page. | |
| skill_revision_id | Yes | Skill revision 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 the operation as read-only, idempotent, and non-destructive, lowering the burden on the description. The description adds meaningful behavioral context about the pagination contract, explaining how to use `has_more` and `next_cursor` to continue fetching pages.
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 filler. The core operation is stated first, and the pagination detail that is essential to correct invocation is placed right after.
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 paginated list tool, the description covers the essential return-related behavior by naming `has_more` and `next_cursor`. It doesn't describe the file object shape, but the lack of an output schema is partially mitigated by the simplicity of the operation and the clear resource being listed.
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 input schema provides 100% coverage, documenting all three parameters with descriptions, defaults, and constraints. The description mostly reiterates the cursor behavior already captured in the schema ('Opaque continuation token from a previous response's `next_cursor`'), so it adds little new semantic value.
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 ('files in a skill revision'), and adds pagination context ('one page at a time'). This clearly distinguishes it from siblings like listSkillRevisions (lists revisions) and listSkillFiles (lists files at the skill level).
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 clearly conveys when to use this tool: when you need the files belonging to a specific skill revision. It doesn't explicitly name alternatives or exclusions, but the resource scoping ('in a skill revision') provides clear context for selection.
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.