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roboflow_upload_annotation

Upload annotations to your Roboflow project images. Supports COCO, YOLO, Pascal VOC, CreateML, and Roboflow JSON formats.

Instructions

Attach an annotation to an image.

Supported formats (annotation_format literal):

  • "coco"

  • "yolo" (YOLOv5/v8/v11-compatible .txt)

  • "pascal_voc" (XML)

  • "createml" (Apple CreateML JSON)

  • "roboflow_json"

Pass annotation as a string (raw file contents) or a dict (Python object; we serialize to JSON). Optional labelmap is a class-id to name map Roboflow uses for YOLO formats.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
projectYes
image_idYes
annotationYes
annotation_formatYes
workspaceNo
labelmapNo
dry_runNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It discloses input format details but does not explain side effects (e.g., overwrite or append), authentication, rate limits, or error handling. The existence of an output schema is not leveraged.

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?

The description is concise, with a clear main sentence and a bullet list for formats. Additional notes on annotation and labelmap are well-placed. No extraneous information.

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

Completeness2/5

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

With 7 parameters (4 required) and no annotation coverage, the description omits critical context: prerequisites (image must exist?), behavior on duplicate annotations, and meaning of several parameters. An output schema exists but is not referenced.

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 description coverage is 0%; the description adds meaning for 'annotation_format' (enum values), 'annotation' (string or dict), and 'labelmap' (class-to-name map). However, it does not explain 'project', 'image_id', 'workspace', or 'dry_run', leaving gaps.

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

Purpose4/5

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

The description states 'Attach an annotation to an image' with a clear verb and resource, and lists supported formats. It does not explicitly differentiate from sibling tools, but the purpose is unambiguous.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus other roboflow tools, nor any prerequisites or exclude conditions. The description only explains how to format input.

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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