completeClarityOrganizationDocumentUploadComplete an organization document capture after uploading to GCS; extracts text and stores it as a transcript so it feeds Process Landscape generation
| Name | Required | Description | Default |
|---|---|---|---|
| orgId | Yes | ||
| fileName | Yes | Name of the uploaded document file | |
| interviewId | Yes | The organization interview ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=false, idempotentHint=false, destructiveHint=false. The description adds behavioral context (text extraction, transcript storage) beyond annotations, but does not disclose potential side effects (e.g., triggering Process Landscape generation), idempotency behavior, or authorization requirements. With annotations present, the additive value is modest but positive.
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 that is front-loaded with the action and includes the key outcome. No unnecessary words, but it could be split into two sentences for improved readability. Still, it is efficient and earns its place.
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?
The description explains what the tool does and its downstream purpose, but omits prerequisites (e.g., the need to first create an upload URL via createClarityOrganizationDocumentUploadUrl) and does not hint at the return value (no output schema provided). For a multi-step process, this leaves gaps in completeness.
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 coverage is 67% (2 of 3 parameters have descriptions). The description does not add any parameter-level details beyond what the schema provides; for instance, it does not explain how to obtain interviewId or orgId. Baseline of 3 is appropriate since coverage is between 50% and 80%, and the description does not 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 uses a specific verb ('Complete') and resource ('organization document capture after uploading to GCS') and clearly states the purpose: extracts text and stores it as a transcript for Process Landscape generation. It effectively distinguishes from siblings like completeClarityDocumentUpload and completeClarityTeamDocumentUpload by specifying 'organization'.
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 usage after GCS upload but does not explicitly state when to use this tool versus alternatives like createClarityOrganizationDocumentUploadUrl or completeClarityDocumentUpload. No preconditions, exclusions, or when-not-to-use guidance are provided. The context is implied from the name and sibling list.
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.