completeClarityImageUploadComplete an image capture after uploading it to GCS via a signed URL
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | The clarity process ID | |
| fileName | Yes | Name of the uploaded image 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 establish this is a non-read-only, non-idempotent, non-destructive write. The description adds one genuinely useful behavioral trait beyond that: the hard prerequisite that the GCS upload via signed URL must happen first. However, it does not disclose what 'complete' does to the capture's state, what happens if invoked before the upload, or the consequences of calling twice (relevant given idempotentHint=false).
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?
A single 15-word sentence that front-loads the verb and object, then appends the only necessary context (the GCS signed-URL prerequisite). There is no redundancy with the title, no fluff, and 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 tool with 3 required params, no output schema, and no nested objects, the description covers the basic what and when. But it leaves gaps: it never references the createClarityImageUploadUrl pairing that produces the signed URL, it does not clarify how the three IDs relate to the upload flow, and with no output schema it says nothing about what a successful completion returns or triggers.
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% — each of the three parameters (id, captureId, fileName) has a description in the schema. Per the baseline for high coverage, the description need not repeat param details, and it doesn't. The description adds no extra meaning about which ID maps to which part of the flow, but the schema already carries the documentation burden adequately.
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 uses a specific verb ('Complete') with a specific resource ('an image capture') and adds the scoping context ('after uploading it to GCS via a signed URL'). This cleanly distinguishes it from the sibling family: completeClarityVideoUpload, completeClarityDocumentUpload, and the other complete* tools differ by resource type (image vs video vs document/artifact).
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 phrase 'after uploading it to GCS via a signed URL' gives clear sequencing context: this is the second step of a two-phase flow, implicitly paired with a URL-creating tool like createClarityImageUploadUrl. It does not explicitly name alternatives or state exclusions (e.g., 'use completeClarityVideoUpload for videos'), so it stops short of full when-not-to-use guidance.
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.