createSandboxUploadUrlGet a presigned URL for uploading a file into a sandbox. Use for files larger than 10MB; smaller files can be uploaded directly via POST /v2/sandboxes/:sandbox_id/files.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | Path where the file will be uploaded (e.g. /workspace/data.csv) | |
| sandbox_id | Yes | The sandbox's unique identifier |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide the safety profile (non-read-only, non-destructive). The description adds the size-based usage context but does not disclose further behavioral traits like URL expiration or required permissions. There is no contradiction with annotations.
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?
Two sentences: the first states the core function, the second provides usage threshold and alternative. No unnecessary words, information is front-loaded and easy to parse.
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 two-parameter tool with no output schema, the description covers essential purpose and usage criteria, and even names an alternative upload path. It could clarify what the presigned URL is used for (e.g., HTTP method or validity) but this is not necessary for basic invocation.
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 descriptions cover both parameters fully, and the description does not add additional parameter meaning beyond what is already in the schema. It relies entirely on the structured data for parameter understanding.
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 clearly states that this tool retrieves a presigned URL for uploading a file to a sandbox. It distinguishes itself from direct upload and other upload URL tools via the size threshold and explicit endpoint reference.
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?
Explicitly specifies when to use the tool (files >10MB) and provides an alternative for smaller files (direct POST via the sandbox files endpoint). This gives clear decision logic, though it does not name a sibling MCP tool.
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.