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 |
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 mark it non-read-only and non-destructive; the description adds useful behavioral context by stating it extracts text, stores a transcript, and feeds Process Landscape generation. It does not mention idempotency consequences, but idempotentHint=false already signals that risk.
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, front-loaded sentence states the action, the precondition, the processing behavior, and the downstream consumer with no filler. Every clause contributes to helping the agent decide and invoke the tool.
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 3-parameter, multi-step completion operation with no output schema, the description covers the main workflow and downstream outcome, which is enough for a capable agent. The main gap is that it does not describe the return value or what happens on failure, but these are minor against the clear purpose and schema coverage.
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 describes interviewId and fileName but leaves orgId undocumented at 67% coverage. The description does not add parameter-specific meaning or explain how orgId/interviewId/fileName relate to the capture, so it neither compensates for the missing orgId description nor enriches the covered parameters.
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'), identifies the resource ('organization document capture'), and defines what completion entails: text extraction and transcript storage for Process Landscape generation. This distinguishes it from sibling upload-completion tools like completeClarityTeamDocumentUpload or completeClarityImageUpload.
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 states a clear precondition ('after uploading to GCS') and implies the organization-document workflow, but it does not name alternatives or say when not to use it. An agent must infer that this is the org-specific counterpart to completeClarityDocumentUpload rather than being told.
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.