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container_commit

Save a container's filesystem state as a new Docker image. Use to capture debugging state or manual changes made inside a container.

Instructions

Snapshot a container's current filesystem state as a new image.

Useful for capturing a debugging state or saving manual changes made inside a container. For repeatable builds use image_build with a Dockerfile instead; publish the result with image_tag + image_push. The container is paused by default during the snapshot to ensure filesystem consistency — set pause=False only if the container cannot be paused. changes accepts Dockerfile instructions to apply on top of the snapshot, e.g. ["CMD ["python", "app.py"]", "ENV FOO=bar"].

args: id_or_name - Container id or name to snapshot repository - Repository name for the new image, e.g. "myorg/myimage" tag - Tag for the new image (default: "latest") message - Commit message stored in the image metadata author - Author string stored in the image metadata pause - Pause the container during commit for consistency (default True) changes - Dockerfile instructions (CMD, ENV, EXPOSE, etc.) to apply to the image conf - Additional image configuration overrides as a dict returns: dict - The new image's full inspect payload (Id is the new image id)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagNo
confNo
pauseNo
authorNo
changesNo
messageNo
id_or_nameYes
repositoryNo
Behavior4/5

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

Annotations already indicate readOnlyHint=false, destructiveHint=false. The description adds behavioral details (container pauses by default, pause=False option, changes parameter behavior) beyond annotations, though permissions or side effects are not discussed.

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

Conciseness5/5

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

Well-structured with clear paragraphs, front-loaded purpose, and no redundant sentences. Every sentence adds value.

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

Completeness5/5

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

Given 8 parameters and nested objects, the description covers all key aspects including return value (dict with Id). No output schema, but return format is specified.

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

Parameters5/5

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

All 8 parameters are explained with descriptions, defaults, and examples (e.g., changes accepts Dockerfile instructions, pause default true). The schema has 0% coverage, so the description fully 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?

The description uses a specific verb+resource combo ('Snapshot a container's current filesystem state as a new image') and distinguishes from siblings like image_build and image_tag+push.

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

Usage Guidelines5/5

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

Explicitly states when to use (debugging, manual changes) and provides a clear alternative for repeatable builds with image_build, image_tag, and image_push.

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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