completeClarityDocumentUploadComplete a document capture after uploading to GCS via signed URL
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | The clarity process ID | |
| fileName | Yes | Name of the uploaded document 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 indicate this is a non-read-only, non-idempotent, non-destructive mutation, so there is no contradiction. The description adds the useful precondition that the upload must already have happened, but it does not disclose what state changes occur, whether repeated calls fail, or what downstream effects completion triggers.
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 sentence with the action front-loaded and the key prerequisite included. There is no redundant restatement of the schema or filler.
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 three-parameter completion step with fully documented parameters, the description gives the essential workflow context: this call finalizes a document capture after the GCS signed-URL upload. It does not describe return values or detailed side effects, but those are not necessary for a basic completion call given the schema and annotations.
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 schema covers 100% of parameters with meaningful descriptions: id, captureId, and fileName are all explained. The description adds no parameter-level details beyond the schema, so it meets the baseline for high schema coverage without needing to compensate.
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 identifies a concrete action ('Complete a document capture') and a specific workflow step ('after uploading to GCS via signed URL'), which separates it from upload-creation tools and from image/video completion tools. It does not explicitly contrast with completeClarityOrganizationDocumentUpload or completeClarityTeamDocumentUpload, so some sibling differentiation relies on the tool name.
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 to GCS via signed URL' provides a clear temporal condition and tells the agent when in the workflow this tool should be called. However, it does not name alternative completion tools or state when to prefer the organization/team/artifact variants, leaving routing to inference.
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.