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roboflow_add_reviewed_to_dataset

Add fully-reviewed annotation jobs to dataset by moving approved images into train/valid/test splits. Requires confirm='yes' and session cookie.

Instructions

Add every fully-reviewed annotation job to the dataset.

Moves images from jobs in review with all images approved into the dataset, split randomly at the given global ratios (default 70/20/10). Restrict to specific jobs via job_ids.

Destructive: requires confirm='yes' and a server mode of curate or full. Also requires ROBOFLOW_SESSION_COOKIE (an app.roboflow.com browser-session cookie) because Roboflow has no public API for this action.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
confirmNo
dry_runNo
job_idsNo
projectYes
workspaceNo
test_ratioNo
train_ratioNo
valid_ratioNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses destructive nature, authentication requirement (browser cookie), random splitting at given ratios. No annotations provided, so description fully carries this burden.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Concise single paragraph, front-loaded with primary action. Could be broken into bullet points for clarity, but no excess verbiage.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 8 parameters and no annotations, description covers core behavior, constraints, and key parameters. Does not detail return format (output schema exists but not referenced). Adequate for complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage 0%, but description explains confirm, job_ids, train/valid/test_ratio. Missing explanations for project, workspace, dry_run. Partially compensates.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states the verb 'add' and resource 'dataset', specifies scope 'every fully-reviewed annotation job'. Distinguishes from siblings like upload_image or add_tags.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides context: requires confirm='yes', specific server mode, and ROBOFLOW_SESSION_COOKIE. Mentions optional job_ids restriction. No explicit when-not-to-use, but sufficient guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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