completeClarityVideoUploadComplete a video capture after uploading to GCS via signed URL
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | The clarity process ID | |
| fileName | Yes | Name of the uploaded video file | |
| captureId | Yes | The capture 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 signal that this is a non-read-only, non-idempotent mutation, so the description does not need to repeat those hints. It adds the useful sequencing context that the upload must already have happened, but it does not disclose what 'complete' does to the capture (e.g., status transition, validation, irreversibility).
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, front-loaded sentence with no filler. It leads with the action and resource, then adds the essential precondition, making it efficient for an agent to parse.
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 tool with no output schema and no nested objects, the description plus schema is adequate to invoke the tool: it names the action, the resource, and the required precondition. It could be more explicit about what completing the upload entails or that it pairs with createClarityVideoUploadUrl, but nothing essential is missing for correct invocation.
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 already documents all three parameters with clear descriptions (clarity process ID, capture ID, uploaded file name) at 100% coverage. The description adds no parameter-specific semantics beyond tying fileName to the GCS upload flow, so the 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 states a specific action ('Complete a video capture') and the resource ('video capture'), with an explicit precondition ('after uploading to GCS via signed URL'). The word 'video' plus the completion-vs-creation verb clearly sets it apart from sibling tools like createClarityVideoUploadUrl and the other complete* upload variants.
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?
It clearly indicates the triggering condition: the tool should be called only after the video file has been uploaded to GCS via a signed URL. It does not explicitly name alternative tools or list when-not-to-use scenarios, but the context is sufficient for basic 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.