createClarityVideoUploadUrlCreate a signed URL for uploading a video file directly to GCS
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | The clarity process ID | |
| fileName | Yes | Name of the video file | |
| contentType | Yes | MIME type of the video | |
| extraCaptureRequestId | No | Extra-capture-request id to bind this capture to |
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?
The annotations only provide generic flags (readOnlyHint=false, destructiveHint=false) and do not explain the operation's nature. The description adds meaningful behavioral context: the tool does not upload the video itself but returns a signed URL, and the upload is performed directly to GCS. This helps the agent understand the expected flow, though expiration or lifecycle details are not disclosed.
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, focused sentence that states the core purpose and the key storage destination ('directly to GCS'). There is no filler, repetition, or unnecessary detail, making it easy for an agent to parse quickly.
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 four-parameter tool with no nested objects, the schema fully documents inputs and the description explains the output concept ('signed URL') and the storage target. The main missing piece is explicit guidance on the follow-up completion flow, but the description is complete enough to select and invoke the tool 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 all four parameters already documented meaningfully in the schema (id, fileName, contentType, extraCaptureRequestId). The description does not add parameter-specific detail beyond referring to 'uploading a video file', which the schema already captures. A baseline score of 3 is appropriate because the schema carries the parameter documentation burden.
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 names a specific verb ('Create'), a specific resource ('a signed URL'), and a specific context ('uploading a video file directly to GCS'). This clearly distinguishes it from sibling upload-URL tools like createClarityImageUploadUrl, createClarityDocumentUploadUrl, and createFileUploadUrl without requiring the agent to inspect 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 description implies the usage context: this is the URL-generation step before a video is uploaded directly to GCS, and the sibling completeClarityVideoUpload suggests a follow-up step. However, it does not explicitly say when to choose this over other upload-URL tools or mention the completion step, so the usage guidance remains implicit rather than explicit.
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.